[User] Please transcribe this text

[Assistant] In fact this is, maybe if you start to think about how to classify an image if you don't know any of these techniques, this is perhaps the approach you would take. Is you would subtract images. So in order to know that an image of a cat is different than an image of a dog, you have to compare them.


[User] Please transcribe this text

[Assistant] Given those two images, what's the, what's the way you compare them? One way you could do it is you just subtract it.


[User] Please transcribe this text

[Assistant] And then sum all the pixel-wise differences in the image. Just subtract the intensity of the image pixel by pixel, sum it up.


[User] Please transcribe this text

[Assistant] If that intent, if that difference is really high, that means the images are very different. Using that metric, we can look at CIFAR10 and use it as a classifier, saying,


[User] Please transcribe this text

[Assistant] Based on this difference function, I'm going to find one of the ten bins for a new image that, that is, that has the lowest difference.


[User] Please transcribe this text

[Assistant] Find an image in this data set that is most like the image I have and put it in the same bin as that image is in. So.


[User] Please transcribe this text

[Assistant] Using our image difference classifier, we can actually do pretty good, much better than random, much better than 10%. We can do 35, 38% accuracy. That's the classifier. We have our first classifier.


[User] Please transcribe this text

[Assistant] Let's take our classifier to a whole new level. Instead of comparing it to just


[User] Please transcribe this text

[Assistant] And with convolutional neural networks, we get very close to 100%.


[User] Please transcribe this text

[Assistant] Where neural networks shine, this very task of binning images. It all starts at this basic computational unit. Signal in, each of the signals are weighed, summed, bias added.


[User] Please transcribe this text

[Assistant] And put an input into a nonlinear activation function that produces an output. The nonlinear activation function is key. All of these put together in more and more hidden layers.


[User] Please transcribe this text

[Assistant] By taking a forward pass on examples that have ground truth labels, seeing how close those labels are to the real ground truth, and then punishing the weights that resulted in the incorrect decisions and rewarding the weights that resulted in correct decisions.


[User] Please transcribe this text

[Assistant] For the case of ten examples, the output of the network is ten different values.


[User] Please transcribe this text

[Assistant] And we wanted our network to classify what is in this image of a handwritten digit. Is it one, uh, is zero, one, two, three through nine.


[User] Please transcribe this text

[Assistant] And each of the neurons on the output is responsible for getting really excited when its number is called.


[User] Please transcribe this text

[Assistant] And everybody else is supposed to be not excited. Therefore,


[User] Please transcribe this text

[Assistant] The number of classes is the number of outputs. That's how it's commonly done. And you...


[User] Please transcribe this text

[Assistant] But that's for a fully connected network that we've discussed on Monday.


[User] Please transcribe this text

[Assistant] There is in deep learning a lot of tricks that make things work, that make training much more efficient on large,


[User] Please transcribe this text

[Assistant] Class problems where there's a lot of classes on large data sets when the representation that the neural network is tasked with learning is extremely complex. And that's where convolutional neural networks step in. The trick they use is spatial invariance. They use the idea that


[User] Please transcribe this text

[Assistant] A cat in the top left corner of an image is the same as a cat in the bottom right corner of an image. So we can learn the same features across the image. That's where the convolution operation steps in.


[User] Please transcribe this text

[Assistant] Instead of the fully connected networks, here there's a third dimension of depth. So the blocks in this neural network


[User] Please transcribe this text

[Assistant] As input take 3D volumes and as output produce 3D volumes.


[User] Please transcribe this text

[Assistant] They take a slice of the image, a window, and slide it across.


[User] Please transcribe this text

[Assistant] Applying the same exact weights, and we'll go through an example. The same exact weights as in the fully connected network on the edges that are used to map the input to the output. Here are used to map this slice of an image, this window of an image to the output.


[User] Please transcribe this text

[Assistant] And you can make several, many of such convolutional filters. Many layers, many different options of what kind of features you look for in an image. What kind of window you slide across in order to extract all kinds of things.


[User] Please transcribe this text

[Assistant] All kinds of edges. All kinds of higher order patterns in the images.


[User] Please transcribe this text

[Assistant] The very important thing is the parameters on each of these filters, these subset of the image, these windows, are shared. If the feature


[User] Please transcribe this text

[Assistant] That defines a cat is useful in the top left corner, it's useful in the top right corner, it's useful in every aspect of the image. This is the trick that makes convolutional neural networks save a lot of


[User] Please transcribe this text

[Assistant] A lot of parameters, reduce parameters significantly.


[User] Please transcribe this text

[Assistant] It's the reuse, the spatial sharing of features across the space of the image.


[User] Please transcribe this text

[Assistant] The stride is the skip of the filter, the step size, how many pixels you skip when you apply the filter to the input and the padding.


[User] Please transcribe this text

[Assistant] Is the padding, the zero padding on the outside of the input to a convolutional layer. Let's go through an example.


[User] Please transcribe this text

[Assistant] And the slides are now available online, you can follow them along. And I'll step through this example.


[User] Please transcribe this text

[Assistant] On the left here is input volume of three channels.


[User] Please transcribe this text

[Assistant] The left column is the input. The three blocks, the three squares there are the three channels.


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[Assistant] And there's numbers inside those channels.


[User] Please transcribe this text

[Assistant] Two of them. Two channels of filters. With a bias.


[User] Please transcribe this text

[Assistant] And we, those filters are three by three. Each one of them is size three by three. And what we do is we take those three by three filters


[User] Please transcribe this text

[Assistant] That are to be learned. These are our variables, our weights that we have to learn.


[User] Please transcribe this text

[Assistant] And then we slide it across an image to produce the output on the right, the green.


[User] Please transcribe this text

[Assistant] So by applying the filters in the red, there's two of them, and within each one there's one per every input channel. We go from the left to the right.


[User] Please transcribe this text

[Assistant] And you can look, uh, you can pull up the slides yourself now if you can't see the numbers on the screen. But the, the operations


[User] Please transcribe this text

[Assistant] Are performed on the input to produce the single value that's highlighted there in the green in the output. And we slide this convolution, no filter, along the image. With a stride, in this case,


[User] Please transcribe this text

[Assistant] Of two. Skipping, skipping along.


[User] Please transcribe this text

[Assistant] They sum to the, to the right, the two channel output, uh, in green. That's it, that's the convolutional operation.


[User] Please transcribe this text

[Assistant] That's what's called the convolutional layer neural networks. And the parameters here, besides the bias, are the red values in the middle. That's what we're trying to learn.


[User] Please transcribe this text

[Assistant] And there's a lot of interesting tricks we'll discuss today on top of those. But this is at the core. This is the spatially invariant sharing of parameters that make convolutional neural networks able to efficiently learn and find patterns and images. To build your intuition a little bit more,


[User] Please transcribe this text

[Assistant] About convolution. Here's an input image on the left. And on the right, the identity filter.


[User] Please transcribe this text

[Assistant] Produces the output you see on the right. And then there's different ways you can, different kinds of edges you can extract.


[User] Please transcribe this text

[Assistant] With the activate, with the resulting activation map seen on the right. So when applying the filters, with those edge detection filters to the image on the left, you produce in white are the parts that, uh, activate the convolution. The results of these filters.


[User] Please transcribe this text

[Assistant] And so you can do any kind of filter. That's what we're trying to learn. Any kind of edge. Any kind of...


[User] Please transcribe this text

[Assistant] Any kind of pattern you can move along in this window, in this way that's shown here. You slide around the image and you produce the output you see on the right.


[User] Please transcribe this text

[Assistant] And depending on how many filters you have in every level, you have many of such slices that you see on the right. The input on the left, the output on the right. If you have


[User] Please transcribe this text

[Assistant] Dozens of filters, you have dozens of images on the right, each with different results that show


[User] Please transcribe this text

[Assistant] Uh, where each of the individual filter patterns were found. And we learn what patterns are useful to look for in order to perform the classification task. That's the task for the neural network, to learn these filters.


[User] Please transcribe this text

[Assistant] And the filters have higher and higher order of representation.


[User] Please transcribe this text

[Assistant] Going from the very basic edges to the high semantic meaning that spans entire images.


[User] Please transcribe this text

[Assistant] And the ability to span images can be done in several ways, but traditionally has been successfully done through max pooling, through pooling.


[User] Please transcribe this text

[Assistant] Of taking the output of a convolutional operation.


[User] Please transcribe this text

[Assistant] And reducing the resolution of that by...


[User] Please transcribe this text

[Assistant] By condensing that information by, for example, taking the maximum values, the maximum activations.


[User] Please transcribe this text

[Assistant] Therefore reducing the spatial resolution, which has detrimental effects as we'll talk about in scene segmentation, but it's beneficial for finding higher order representations in the images that bring images together. That bring features together to form an entity that we're trying to identify and classify. Okay, so that forms


[User] Please transcribe this text

[Assistant] A convolutional neural network. Such convolutional layers stacked on top of each other is the only addition to a neural network that makes for a convolutional neural network.


[User] Please transcribe this text

[Assistant] And then at the end, the fully connected layers or any kind of other, uh, architectures allow us to apply particular domains. Let's take ImageNet as a case study.


[User] Please transcribe this text

[Assistant] An ImageNet, the data set, an ImageNet, the challenge, the task is classification.


[User] Please transcribe this text

[Assistant] As I mentioned in the first lecture, imaging as a data set.


[User] Please transcribe this text

[Assistant] One of the largest in the world of images. With 14 million images, 21,000 categories.


[User] Please transcribe this text

[Assistant] And a lot of depth to many of the categories. As I mentioned, 1200 Granny Smith apples.


[User] Please transcribe this text

[Assistant] These allow to, these allow the neural networks to learn the rich representations in both pose, lighting variability and intraclass, class variation for the particular things, particular classes like Granny Smith apples.


[User] Please transcribe this text

[Assistant] So, let's look through the various networks, let's discuss them, let's see the insights. It started with AlexNet, the first really big successful GPU-trained neural network on ImageNet that's achieved a significant boost over the previous year. And moved on to VGGNet, GoogleNet, Agulinet,


[User] Please transcribe this text

[Assistant] Again, the numbers will show for the accuracy are based on the top five error rate. We get five guesses, and it's a one or zero. If you get a guess, if one of the five is correct, you get a one for that particular guess, otherwise it's a zero.


[User] Please transcribe this text

[Assistant] And human error is 5.1. When a human tries to achieve the same, tries to perform the same task as the machinist's task we're doing, the error is 5.1. The human annotation is performed on the images based on binary classification. Granny Smith Apple or not.


[User] Please transcribe this text

[Assistant] Cat or not. The actual task that the machine has to perform and that the human competing has to perform is given an image is provided one of the many classes.


[User] Please transcribe this text

[Assistant] Under that, human error is 5.1%, which was surpassed in 2015 by Resnet, uh, to achieve 4% error.


[User] Please transcribe this text

[Assistant] So let's start with AlexNet. I'll zoom in on the later networks. They have some interesting insights. But AlexNet and VGGNet both followed a very similar architecture. Very uniform throughout its depth.


[User] Please transcribe this text

[Assistant] VGG Net in 2014 is convolution, convolution, pooling, convolution, pooling, convolution, pooling, and fully connected layers at the end.


[User] Please transcribe this text

[Assistant] There's a certain kind of beautiful simplicity, uniformity to these architectures. Because you can just make it deeper and deeper and makes it very amenable to, uh,


[User] Please transcribe this text

[Assistant] Implementation in a layered stack kind of way in any of the deep learning frameworks.


[User] Please transcribe this text

[Assistant] It's clean and beautiful to understand. In the case of VGGNet, 16 or 19 layers with 138 million parameters. Not many optimizations on these parameters, therefore, the number of parameters is much higher than the networks that followed it, despite the layers not being that large.


[User] Please transcribe this text

[Assistant] Google net introduced the inception module, starting to do some interesting things with the small modules within these networks, which allow for the training to be more efficient and effective.


[User] Please transcribe this text

[Assistant] The idea behind the inception module shown here, with the previous layer on bottom,


[User] Please transcribe this text

[Assistant] And the convolutional layer here with the inception module on top.


[User] Please transcribe this text

[Assistant] Different sized convolutions provide different value for the network.


[User] Please transcribe this text

[Assistant] Smaller convolutions are able to capture or propagate forward features that are very local.


[User] Please transcribe this text

[Assistant] Larger convolutions are better able to, to represent and capture and catch highly abstracted features, higher order features.


[User] Please transcribe this text

[Assistant] So the idea behind the inception module is to say, well, as opposed to choosing in a hyperparameter tuning process or architecture design process, choosing which convolution size we want to go with,


[User] Please transcribe this text

[Assistant] Why not do all of them together? Well, several together. In the case of the Google net model, there's the one by one, three by three and five by five convolutions.


[User] Please transcribe this text

[Assistant] With the old trusty friend of Max Pooling still left in there as well.


[User] Please transcribe this text

[Assistant] Which has lost favor more and more over time for the image classification task.


[User] Please transcribe this text

[Assistant] And the result is there's fewer parameters that are required. If you pick the placing of these inception modules correctly, the number of parameters required to achieve a higher performance is much lower.


[User] Please transcribe this text

[Assistant] ResNet, one of the most popular still-to-date


[User] Please transcribe this text

[Assistant] Architectures that we'll discuss in scene segmentation as well.


[User] Please transcribe this text

[Assistant] Increases representation power. So these residual blocks allow you to have much deeper networks. And I'll explain why in a second here. But.


[User] Please transcribe this text

[Assistant] The thought was they work so well because the networks are much deeper.


[User] Please transcribe this text

[Assistant] The key thing that makes these blocks so effective is the same idea that's reminiscent of recurrent neural networks that I hope we get a chance to talk about. The training of them is much easier.


[User] Please transcribe this text

[Assistant] They take a simple block, repeat it over and over.


[User] Please transcribe this text

[Assistant] And they pass the input along without transformation.


[User] Please transcribe this text

[Assistant] Along with the ability to transform, to learn, to learn the filters, learn the weights. So you're allowed to...


[User] Please transcribe this text

[Assistant] You allow every layer to not only take on the processing of previous layers, but to take in the raw and transform data and learn something new.


[User] Please transcribe this text

[Assistant] The ability to learn something new allows you to have much deeper networks.


[User] Please transcribe this text

[Assistant] And the simplicity of the block allows for more effective training.


[User] Please transcribe this text

[Assistant] That unlike the previous year with CU Image which simply took ensemble methods and combined a lot of successful approaches to take a marginal improvement. SCNet got a significant improvement.


[User] Please transcribe this text

[Assistant] At least in percentages, I think there's a 25% reduction in error.


[User] Please transcribe this text

[Assistant] Something like that. Uh, by using a very simple idea that I think is important to mention. A simple insight.


[User] Please transcribe this text

[Assistant] It added a parameter to each channel in the convolutional layer. In the convolutional block.


[User] Please transcribe this text

[Assistant] This is kind of a takeaway to think about, about any of the networks who talk about any of the architectures.


[User] Please transcribe this text

[Assistant] Is a lot of times, recurrent neural networks and convolutional neural networks have tricks that significantly reduce the number of parameters.


[User] Please transcribe this text

[Assistant] The bulk, the sort of low-hanging fruit, they use spatial invariance or temporal invariance to reduce the number of parameters to represent the input data. But they also leave certain things not parametrized. They don't allow the network to learn it.


[User] Please transcribe this text

[Assistant] Allow in this case the network to learn the weighting on each of the individual channels. So each of the individual filters is something that you learn as along with the filters.


[User] Please transcribe this text

[Assistant] Takes a, makes a huge boost. The cool thing about this is it's applicable to any architecture. This kind of block, this kind of, what the, the squeeze and excitation block is applicable to any architecture.


[User] Please transcribe this text

[Assistant] And, uh, cause obviously it's, uh, it's just simply parameterizes the ability to choose which filter you go with based on the content.


[User] Please transcribe this text

[Assistant] It's a subtle but crucial thing. I think it's pretty cool. And for future research, it inspires to think about


[User] Please transcribe this text

[Assistant] Uh, what else can be parameterized in neural networks? What else can be controlled as part of the learning process? Including higher and higher order hyperparameters. Which, which aspects of the training and the architecture of the network can be part of the learning? This is what this network inspires.


[User] Please transcribe this text

[Assistant] Another network that's been in development since the 90s, ideas.


[User] Please transcribe this text

[Assistant] But Jeff Hinton, but really received, has been published on and received significant attention in 2017 that I won't go into detail here. Uh, we are going to release, uh,


[User] Please transcribe this text

[Assistant] An online only video about capsule networks. It's a little bit too technical, but they inspire a very important point.


[User] Please transcribe this text

[Assistant] That we should always think about with deep learning, uh, whenever it's successful, is to think about what, as I mentioned with the cat eating a banana,


[User] Please transcribe this text

[Assistant] On a philosophical and a mathematical level, we have to consider what assumptions these networks make and what, through those assumptions, they throw away.


[User] Please transcribe this text

[Assistant] So neural networks due to the spatial, with convolutional neural networks, due to their spatial invariance, throw away information about the relationship between


[User] Please transcribe this text

[Assistant] The, the hierarchies between the simple and the complex objects. So the face on the left and the face on the right looks the same to a convolutional neural network.


[User] Please transcribe this text

[Assistant] The presence of eyes and nose and mouth.


[User] Please transcribe this text

[Assistant] Is the essential aspect of what makes the classification task work for convolutional network, where it will fire and say this is definitely a phase. But the spatial relationship is lost, is ignored, which means there's a lot of implications to this, but for things like pose variation,


[User] Please transcribe this text

[Assistant] That information is lost. We're throwing that away completely and hoping that the pooling operation


[User] Please transcribe this text

[Assistant] That's performed in these networks is able to sort of mesh everything together to come up with the features that are firing on different parts of the face and then come up with the total classification that it's a face without representing really the relationship between these features at the low level and the high level.


[User] Please transcribe this text

[Assistant] At the low level of the hierarchy, the simple and the complex level.


[User] Please transcribe this text

[Assistant] This is a super exciting field now that hopefully will spark developments of how we design neural networks that are able to learn the rotational, the orientation invariance as well.


[User] Please transcribe this text

[Assistant] You take these convolutional neural networks, chop off the final layer in order to apply it to a particular domain.


[User] Please transcribe this text

[Assistant] And that is what we'll do with fully convolutional neural networks. The ones that we task to segment the image at a pixel level.


[User] Please transcribe this text

[Assistant] As a reminder, these networks through the convolutional process,


[User] Please transcribe this text

[Assistant] Are really producing a heat map. Different parts of the network are getting excited based on the different aspects of the image. And so it can be used to do the localization of detecting, not just classifying the image, but localizing the object. And it could do so at a pixel level.


[User] Please transcribe this text

[Assistant] So the convolutional layers are doing the encoding process. They're taking the rich raw sensory information.


[User] Please transcribe this text

[Assistant] In the image and encoding them into an interpretable set of features, uh, representation that can then be used for classification. But we can also then use a decoder, up sample that information and produce a map like this.


[User] Please transcribe this text

[Assistant] Fully convolutional neural network, segmentation, semantic scene segmentation, image segmentation. The goal is to, as opposed to classify the entire image, you classify every single pixel.


[User] Please transcribe this text

[Assistant] It's pixel level segmentation. You color every single pixel with what that pixel, what object that pixel belongs to in this 2D space of the image.


[User] Please transcribe this text

[Assistant] The 2D projection, the, uh, in the image of a three-dimensional world.


[User] Please transcribe this text

[Assistant] So the thing is, uh, there's been a lot of advancement in the last three years, but it's still an incredibly difficult problem. If you think, if you think about


[User] Please transcribe this text

[Assistant] Uh, the amount of data that's used for training and the task of pixel level, uh, of megapixels here of millions of pixels that are tasked with having assigned a single label. It's an extremely difficult problem.


[User] Please transcribe this text

[Assistant] Why is this an interesting, important problem to try to solve, as opposed to bounty boxes around cats?


[User] Please transcribe this text

[Assistant] Well, it's whenever precise boundaries of objects are important. Certainly medical applications when looking at imaging and detecting particular, for example, detecting tumors in, uh, in medical imaging of different, uh, different organs. And in driving, in robotics,


[User] Please transcribe this text

[Assistant] When objects are involved, it's a dense scene involved with vehicles, pedestrians, cyclists, we need to be able to not just have a loose estimate of where objects are, we need to be able to have the exact boundaries. And then potentially,


[User] Please transcribe this text

[Assistant] A lot of this work successfully, a lot of the work in the semantic segmentation started with fully convolutional networks for semantic segmentation paper, FCN. That's where the name FCN came from in November 2014. Now go through a few papers here to give you some intuition where the field is gone.


[User] Please transcribe this text

[Assistant] And how that takes us to SegFuse, the segmentation competition.


[User] Please transcribe this text

[Assistant] So FCN repurposed the ImageNet pre-trained nets. The nets that were trained to classify what's in an image.


[User] Please transcribe this text

[Assistant] The entire image. And chopped off the fully connected layers. And then added decoder parts that up sampled the image. To produce a heat map.


[User] Please transcribe this text

[Assistant] Here shown with a tabby cat, a heat map of where the cat is in the image.


[User] Please transcribe this text

[Assistant] It's a much slower, much coarser resolution than the input image. 1.8 at best.


[User] Please transcribe this text

[Assistant] Skip connections to improve coarseness of up sampling. There's, uh, a few tricks.


[User] Please transcribe this text

[Assistant] If you do the most naive approach, the upsampling is going to be extremely coarse. Because that's the whole point of the neural network. The encoding part is you throw away all the useless data.


[User] Please transcribe this text

[Assistant] Pooling operations to go in similar ways as a residual block to, uh, go, to, go to the output produce higher and higher, uh, resolution heatmap at the end. SegNet in 2015,


[User] Please transcribe this text

[Assistant] Applied this to the driving context and really taken it to kitty dataset and have shown a lot of interesting results and really explored the encoder decoder formulation of the problem.


[User] Please transcribe this text

[Assistant] Really solidifying this, the place of the encoder-decoder framework for the segmentation task.


[User] Please transcribe this text

[Assistant] Dilated convolution, I'm taking you through a few components which are critical here to the state of the art. Dilated convolutions, so the convolution operation as the pooling operation reduces resolution significantly.


[User] Please transcribe this text

[Assistant] And dilated convolution has a certain kind of gritting as visualized there that maintains the, the local high resolution textures.


[User] Please transcribe this text

[Assistant] It's called Dilated Convolutional Layer. And that's, in a 2015 paper, proved to be much better at upsampling a high resolution image.


[User] Please transcribe this text

[Assistant] Added conditional random fields, which is the final piece of the, uh, of the state of the art puzzle here. A lot of the successful networks today that do segmentation, not all,


[User] Please transcribe this text

[Assistant] Do post-process using, uh, CRFs, conditional random fields. And what they do is they smooth the segmentation, the up-sample segmentation that results from the FCN by looking at the underlying image intensities.


[User] Please transcribe this text

[Assistant] So that's the key aspects of the successful approaches today. You have the encoder decoder framework of a fully convolutional neural network. It replaces the fully connected layers with the convolutional layers.


[User] Please transcribe this text

[Assistant] Deconvolutionary layers. And as the years progress from 2014 to today,


[User] Please transcribe this text

[Assistant] As usual, the underlying networks from AlexNet to VGGnet and to now ResNet have been one of the big reasons for the improvements of these networks to be able to perform the segmentation.


[User] Please transcribe this text

[Assistant] So naturally they mirrored the ImageNet challenge performance in, uh, adapting these networks. So the state of the art uses ResNet or similar networks. Conditional random fields.


[User] Please transcribe this text

[Assistant] For smoothing based on the input image intensities and the dilated


[User] Please transcribe this text

[Assistant] Convolution that maintains the computational cost but increases the resolution of the upsampling throughout the intermediate feature maps.


[User] Please transcribe this text

[Assistant] And that takes us to the state of the art that we used to produce the images.


[User] Please transcribe this text

[Assistant] Uh, to produce the images for the competition.


[User] Please transcribe this text

[Assistant] ResNet-DUC for dance upsampling convolution. Instead of bilinear upsampling, you make the upsampling learnable.


[User] Please transcribe this text

[Assistant] You learn the upscaling filters. That's on the bottom. That's really the key part that made it work.


[User] Please transcribe this text

[Assistant] Sometimes the biggest addition that can be done is parameterizing one of the aspects of the network that you've taken for granted. Letting the network learn that aspect. And the other...


[User] Please transcribe this text

[Assistant] I'm not sure how important it is to the success, but it's a cool little addition. It's a hybrid dilated convolution.


[User] Please transcribe this text

[Assistant] As I showed that visualization where the convolution is spread apart a little bit in the input from the input to the output.


[User] Please transcribe this text

[Assistant] When they're changed, it produces a smoother result. Because when it's kept the same,


[User] Please transcribe this text

[Assistant] There, certain input pixels get a lot more attention than others. So losing that favoritism is what's achieved by using a variable different dilation rate.


[User] Please transcribe this text

[Assistant] Those are the two tricks, but really the biggest one is the parametrization of the upscaling filters.


[User] Please transcribe this text

[Assistant] Okay, so that's what we're, that's what we use to generate that data and that's what we provide you the code with, uh, if you're interested in competing in PsychFuse.


[User] Please transcribe this text

[Assistant] The other aspect here, that everything we talked about from the classification to the, uh, segmentation to making sense of images is, the information about, uh, time, the temporal dynamics of the scene is thrown away.


[User] Please transcribe this text

[Assistant] And for the driving context, for the robotics context, and what we'd like to do with SegFuse for the segmentation, dynamic scene segmentation context of when you try to interpret what's going on in the scene over time and use that information. Time is essential.


[User] Please transcribe this text

[Assistant] Through the 2D projection of an image is fascinating and there's a lot of set of open problems there. So flow


[User] Please transcribe this text

[Assistant] Is what's very helpful to, as a starting point to help us understand how these pixels move. Flow, optical flow.


[User] Please transcribe this text

[Assistant] Dense optical flows, the computation, our best...


[User] Please transcribe this text

[Assistant] There's two images in 30 frames a second. There's one image at time zero. The other is 33.3 milliseconds later. And the dense optical flow is our best estimate of how each pixel in the input image moved to in the output image.


[User] Please transcribe this text

[Assistant] The optical flow for every pixel produces a direction of where we think that pixel moved and the magnitude of how far it moved. That allows us to take information that we detected about the first frame and try to propagate it forward.


[User] Please transcribe this text

[Assistant] This is the competition. It's to try to segment an image and propagate that information forward. For manual annotation


[User] Please transcribe this text

[Assistant] Of a, of a image, so this kind of coloring book annotation where you color every single pixel in a state of the art data set.


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[Assistant] For driving cityscapes that it takes 1.5, 1.5 hours, 90 minutes to do that coloring. That's 90 minutes per image.


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[Assistant] That's extremely long time. That's why there doesn't exist today a data set. And in this class we're going to create one.


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[Assistant] Of segmentation of these images. Through time. Through video.


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[Assistant] So long videos where every single frame is fully segmented. That's still an open problem that we need to solve.


[User] Please transcribe this text

[Assistant] Slow is a piece of that. And we also provide you


[User] Please transcribe this text

[Assistant] The, this computed state of the art flow using flow net 2.0.


[User] Please transcribe this text

[Assistant] Use neural networks to learn the optical flow, the dense optical flow.


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[Assistant] And it did so with two kinds of architectures. Flow net S, flow net simple, and flow net core, flow net C.


[User] Please transcribe this text

[Assistant] The, uh, simple one is simply taking the two images, so what's, what's the task here? There's two images, and you, you want to produce from those two images, they follow each other in time, 33.3 milliseconds apart, and, uh, your task is, is the output to produce the dense optical flow.


[User] Please transcribe this text

[Assistant] So for the simple architecture, you just stack them together. Each are RGB, so it produces a six-channel input to the network. There's a lot of convolution. And finally, it's the same kind of process as the fully convolution neural networks to produce the optical flow. Then there is flow net correlation.


[User] Please transcribe this text

[Assistant] architecture where you perform some convolutions separately before using a correlation layer to combine the feature maps.


[User] Please transcribe this text

[Assistant] And different data sets and different applications. So FlowNet 2.0 in December 2016 is one of the state of the art frameworks, code bases that we use to generate the data I'll show.


[User] Please transcribe this text

[Assistant] Combines the FlowNet S and FlowNet C, and improves over the initial FlowNet, producing a smoother flow field, preserves the fine motion detail along the edges of the objects,


[User] Please transcribe this text

[Assistant] And it runs extremely efficiently. Depending on the architecture, there's a few variants, either 8 to 140 frames a second.


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[Assistant] And the process there is essentially, one that's common across various applications of deep learning, is stacking these networks together.


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[Assistant] The very interesting aspect here that


[User] Please transcribe this text

[Assistant] We're still exploring and again applicable in all of deep learning. In this case, it seemed that there was a strong effect in taking sparse, small, multiple data set and doing the training. The order of which those data sets were used for the training process mattered a lot. That's very interesting.


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[Assistant] So, using FlowNet 2.0, here's the data set we're making available for PsychFuse the competition.


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[Assistant] Cars that MIT died to use slash secfuse. First the original video. Us driving in high definition.


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[Assistant] Original video, driving around, uh, Cambridge. Then we're providing the ground truth for a training set.


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[Assistant] So that training set for every single frame, 30 frames a second, we're providing the segmentation frame to frame to frame. Segmented on mechanical turq. We're also providing the output.


[User] Please transcribe this text

[Assistant] Of the network that I mentioned, the state of the art segmentation network. That's pretty damn close to the ground truth. But still not.


[User] Please transcribe this text

[Assistant] And our task is, this is the interesting thing is,


[User] Please transcribe this text

[Assistant] Well there's two options. One is to take the output of this network


[User] Please transcribe this text

[Assistant] And use other networks to help you propagate the information better. So with this segmentation,


[User] Please transcribe this text

[Assistant] The output of this network does is it only takes a frame by frame by frame. It's not using the temporal information at all.


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[Assistant] So the question is, can we figure out a way, can we figure out tricks to use temporal information to improve this segmentation so it looks more like this segmentation?


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[Assistant] And we're also providing the optical flow from frame to frame to frame. So the optical flow based on flow net 2.0 of how each of the pixels moved.


[User] Please transcribe this text

[Assistant] Okay, and that forms the SegFuse competition. Ten thousand images, and the task is to submit


[User] Please transcribe this text

[Assistant] To take in the original video. Take in for the training set, the ground truth.


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[Assistant] The segmentation from the state of the art segmentation network. The optical flow from the state of the art optical flow network.


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[Assistant] And taking that together to improve the stuff on the bottom left, the segmentation. To try to achieve the ground truth on the top right.


[User] Please transcribe this text

[Assistant] I'd like to thank you. Tomorrow at 1pm is Waymo in Stata.


[User] Please transcribe this text

[Assistant] 32123, the next lecture next week will be on deep learning for sensing the human, understanding the human, and we will release online only lecture on capsule networks and GANs, general adversarial networks. Thank you very much.


[User] Please transcribe this text

[Assistant] Hello, I'm super happy to be here, be part of this wonderful conference and I'm also very happy to introduce the panel on post-labour.


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[Assistant] Uh, and the three, uh, great speakers that will, uh, present on their different expertise. Since I am from the field of, uh, architecture, I would like, uh, first to introduce with, uh, a few thoughts about the architecture of, uh, post-labor.


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[Assistant] Because I think, uh, there are, uh, clear, uh, spatial implications to this new, uh, condition that we were discussing also, uh, with the previous, uh, speaker. I was myself struck a few years, uh, uh, ago.


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[Assistant] With this question and the architectural implications of, uh, of this question, when I read by chance in the, in the Wall Street Journal that 80%, 80% of young professionals, uh, uh, in New York City, uh, were working regularly from their beds.


[User] Please transcribe this text

[Assistant] And this, uh, was actually 2012, so we are talking, uh, it's not moving.


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[Assistant] A newspaper article, uh, the Wall Street Journal in 2012, so it's probably by now a very very conservative


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[Assistant] For the private person living in space becomes for the first time antithetical to the place of work.


[User] Please transcribe this text

[Assistant] The phantasmagoria of the interior. For the private individual, the private environment represents the universe. In it, it gathers remote places and the past. His living room is a box in the world theater.


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[Assistant] Of course there are many precedents for, for this that I don't have time to go in, but think for example about Hugh Hefner, who, uh, died, uh, recently. And here you have him working in bed in the, um, in the, uh, fifties when he started to do the magazine, he already kind of inaugurated the idea


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[Assistant] Of the bed as a play, as an office, right? We're full of, uh, of all kinds of media, uh, equipment.


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[Assistant] As if in response to these, uh, new realities, uh, of, of today, uh, returning to our time, office spaces are being domesticated to, with a sleeping pod.


[User] Please transcribe this text

[Assistant] The ubiquitous cell phone means that people are constantly carrying private spaces between them but also engaging in new forms of collectivity, navigating the city and interacting with it on the basis of feedback from huge numbers of people.


[User] Please transcribe this text

[Assistant] For a metro, for a train somewhere in Asia, but at the same time they are also in another kind of space that we all inhabit now, uh, without thinking.


[User] Please transcribe this text

[Assistant] The 19th century division of the city, uh, between rest and work.


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[Assistant] Uhm, wrote about, uh, uh, uh, uh, this. He say, they replace horses, didn't they? Eh, and, and nobody took him, uh, uh, seriously. He basically say that the human worker will go the way of the horse.


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[Assistant] He ended up getting the Nobel Prize, but nobody paid much attention. But recently, the New York Times reconsidered the idea of the end of the human war horse.


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[Assistant] Uh, universal income are underway in places from California, uh, to Finland.


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[Assistant] Ironically, it was already in vision in utopian projects that we have mentioned before also of the 60s and 70s by Constant, by Superstudio, by Archizoon, by Archigram, those are all


[User] Please transcribe this text

[Assistant] Projects, uh, that I have images now on the screen from ArchiGram, for example, that all have this hyper-equipped


[User] Please transcribe this text

[Assistant] Between the bed inserted in the office and the office inserted in the bed, a whole new horizontal architecture has taken over in a collapse of traditional distinctions between private and public, between work and play, between rest and action.


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[Assistant] Finally, uh, Hiroshi Yamakawa, uh, is director of Duango Artificial Intelligence Laboratory and a chairperson of the Whole Brain Architecture Initiative, as well as director and managing editor of the Japanese Society for Artificial Intelligence.


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[Assistant] Of any organization and leaders who are part of any organization, uh, have to have a set of priorities and there has to be prioritization within the organization.


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[Assistant] Uh, if an organization and a company are chasing too many things,


[User] Please transcribe this text

[Assistant] Uh, it becomes very dysfunctional very quickly. So first off, the leader has to establish what are the priorities of the organization. And, uh, it can't be ten things. It's probably three to five. In terms of big, you know, audacious goals.


[User] Please transcribe this text

[Assistant] And then you manage against that. On a personal level, uh,


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[Assistant] I think you want to spend your time where you're really maximizing the leverage of your ability to engage in the business. Where can you have the most impact?


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[Assistant] Uhm, and where do you have something, uh, to really add that's critical to the prioritization of the organization?


[User] Please transcribe this text

[Assistant] Uhm, invariably, you're going to have a challenge


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[Assistant] Somewhere in your organization and I think you want to step in there and see if you can help.


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[Assistant] Overcome what those challenges are. And I think, uh,


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[Assistant] It's important every now and then to do a kind of a personal assessment, uh, of just how am I spending my time? You might want to look back at a month. Just look at your calendar. Where was I this month?


[User] Please transcribe this text

[Assistant] And, and, and do a, an assessment of whether or not you think.


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[Assistant] What you have prioritized are the right things. Have you had the impact that you think you want it to have?


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[Assistant] And uhm, is there an opportunity to make an adjustment?


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[Assistant] Every industry and every business pretty much has a financial model, uh, for the unit economics, the cost of goods, uh, and ultimately the profitability of the business. So let's take a retail brick and mortar business, which I know a fair amount about.


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[Assistant] But having said that, brick and mortar business are not going away. They just have to be, you gotta be much better than you were ten years ago to be a primary destination. But the unit economics, uh, are the following. The sales to investment ratio of a brick and mortar business, uh, the best of class is two to one.


[User] Please transcribe this text

[Assistant] So, two times sales to the investment of building the store. That's it. Second, if you can achieve a 20% fully loaded operating margin of that retail store, you are best of class.


[User] Please transcribe this text

[Assistant] So, if you're opening a retail store and you're


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[Assistant] And your sales are a million dollars, first year. You don't want to build that store for more than 500,000.


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[Assistant] And you want to get a 20% $200,000 profit.


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[Assistant] And you are best of class. If you know that's best of class and you're at one to one or 10% operating profit, you know your business model does not work.


[User] Please transcribe this text

[Assistant] That's one example. If you are a consumer products company today, you're under tremendous margin pressure. And so, you really have to make sure that the hidden cost of the cost of the product


[User] Please transcribe this text

[Assistant] Are something you know a lot about. And specifically what I'm talking about is the supply chain aspect of the business.


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[Assistant] And, uh, I think one of the great lessons is most people focus so much on the cost of the product to make it and the retail selling price and you lose sight of the cost of getting it to market.


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[Assistant] There shouldn't be too many financial surprises.


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[Assistant] If as leaders we're doing our job, because you're constantly getting fed information.


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[Assistant] And if you're not paying attention and that monthly or quarterly report is a surprise to you, uh, I think that's on you. However, the income statement on the balance sheet is just one metric.


[User] Please transcribe this text

[Assistant] Which obviously is the financial performance of the company. The other metric is what's the, what is the health and, uh,


[User] Please transcribe this text

[Assistant] Uh, issue that you've got to know and feel. You can't isolate yourself.


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[Assistant] Uh, you gotta be very visible, especially in the early stages of all this, to not only know what's going on, but engage with people and ask probing questions about the business, the business unit,


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[Assistant] Uh, the area of responsibility they have so that you should never be really that surprised.


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[Assistant] About what's going on. This goes back to, you know, what it means to be curious. Uh, I'm not trying to surprise people or, uh, but you learn a lot.


[User] Please transcribe this text

[Assistant] Uh, by engaging with people not in a formal way.


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[Assistant] You don't have to call anybody in your office, you can just go out. And you'll be amazed about...


[User] Please transcribe this text

[Assistant] What people will share with you and what you're going to learn in these conversations that are informal. I've used the expression many times, you've got to be in the mud. You've got to get your hands dirty. You've got to be in the mud.


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[Assistant] And I once gave a speech to all of our people in 2009, I said, the, the, the, I never, I don't think I've ever did that before, where I said, the name of this speech, the name of this talk is being in the mud. I want to get our hands dirty. I don't want to be operating at 20,000 feet. I want to be in the mud. Where...


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[Assistant] We really can understand what's going on. Get to the root of the issues. The root of the problem.


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[Assistant] In every leadership team meeting that I've had for the last almost 40 years on Monday morning and every board meeting that we've had every quarter.


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[Assistant] I've had two empty chairs in my mind in that room.


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[Assistant] One share is occupied by a Starbucks employee who we call partner, and one is by a customer. The shareholders are represented by the board. They're already in the room.


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[Assistant] And I asked myself in every leadership team meeting, in every board meeting,


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[Assistant] Is this decision, is this strategy that we're about to make or embark on going to make our people and our customers proud? And if the answer is yes, you know you're on the right track. If the answer is no, despite the enthusiasm of the room,


[User] Please transcribe this text

[Assistant] You know that you need to put up a red flag and say, wait a minute, I'm concerned about this. And if the answer is grey, then you need more debate. Even if it gets a little tension involved in it, you need more debate.


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[Assistant] You don't want to leave those rooms and make a decision, short or long term, that you know in your heart is not going to make your people or your customers proud. You're in the business of making your people and your customers proud. And that will result in a great return for the shareholder.


[User] Please transcribe this text

[Assistant] After reading an article entitled, Cigarette Smoking and Your Health, I lit a cigarette to calm my nerves. I smoked with concentration and pleasure.


[User] Please transcribe this text

[Assistant] As I was sure that this would be my last cigarette. For a whole week I did not smoke at all. And during this time my wife suffered terribly.


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[Assistant] I had all the usual symptoms of someone giving up smoking, a bad temper, and an enormous appetite.


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[Assistant] My friends kept on offering me cigarettes and cigars. They made no effort to hide their amusement


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[Assistant] Whenever I produced a packet of sweets from my pocket.


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[Assistant] After seven days of this, I went to a party. Everybody around me was smoking, and I felt extremely uncomfortable.


[User] Please transcribe this text

[Assistant] When my old friend Brian urged me to accept a cigarette, it was more than I could bear. I took one guiltily, lit it, and smoked with satisfaction.


[User] Please transcribe this text

[Assistant] My wife was delighted that things had returned to normal once more. Anyway, as Brian pointed


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[Assistant] It is the easiest thing in the world to give up smoking. He himself has done it lots of times.


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[Assistant] Written by Mary Ann Marazzi. Illustrated by Rebecca Stuff.


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[Assistant] What we're gonna look at here is the fact that Kingzone is up three turrets to one. So they had a very good pressure draft, which is something they haven't been able to have in the first three games. Getting Uzi on the Kai'Sa while terrifying for the late game means they can actually get some lane pressure.


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[Assistant] Pretty happy about those drakes. There's another Infernal on the map. And there's another Infernal on the way, a couple minutes away. Uzi's at a two item power spike, they have the Shurelya's Reverie on the Malzahar, they also have a Skarner, and there are not many QSS's yet on King's Zone.


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[Assistant] Teamfight, one incredibly minor misplay to get the Baron, but here, having the guts to just go for it. Seeing the AD carry out there, stacking the suppression on him, and at that point, Uzi can unleash.


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[Assistant] Faker has been the best player in the world for four years. People are now calling Uzi there. In history, it's definitely still Faker. If I can see SKT making it back to the World Championship, I can't wait to see what happens if it does, but if it doesn't, this could be the start of something amazing for Uzi as the AD carry.


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[Assistant] Yeah, and I gotta say coming into the tournament based on expectations, this was a massive upset. The fact that RNG win. There was a reason that Korea has this reputation and also the last time Korea dropped the finals at MSI, SKT


[User] Please transcribe this text

[Assistant] Yeah, 14 teams in that league as well. We saw how difficult it was for them to get through the playoffs. This moment right now, I don't necessarily think it's the changing of the guard.


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[Assistant] A slow creep of power from the LPL over worlds. Remember we saw RNG take SKT to five games. We saw WE also make it to the semi-finals last world championship. They were clearly the second best region. One victory does not make you the best region over the LCK. The LCK is going to be hungry and I just can't wait to watch the rematch.


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[Assistant] Yeah, every MSI flash rules has now made it to the knockout stage. Those pool one seeds at Worlds are thankful.


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[Assistant] As far as trying to figure out the slow team versus the fast aggressive maybe helter skelter team.


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[Assistant] It's never been the LPL being the slow champ and the LCK being the fast team. And it's funny how the first time that happens in a best of five, it's the LPL that wins.


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[Assistant] Peanut was dominant in the LCK season. He was not dominant here at MSI. They were subbing in Cuz. I know that he was getting a lot of


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[Assistant] So, how a team deals with failure can often be more illuminating than how a team deals with success. And now Kingzone with this new roster with Peanut has to deal with some failure.


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[Assistant] Wouldn't have expected the Uzi from three years ago to wait for his entire team to grab the trophy. I know that seems like a minor thing, but it has so much been all about Uzi and I think Uzi is doing a good job of deferring and


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[Assistant] Yo guys, it was such a great 2019! Again, thank you, Deutschland, that was super! Nols!


[User] Please transcribe this text

[Assistant] From Portugal, it's straight back to the Netherlands for free festival.


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[Assistant] This is the moment. It's one of the oldest velodromes in the world, but perhaps the most recognizable in world cycling. And this is where the riders in this year's edition of Paris-Roubaix will finish, as they always have over the years.


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[Assistant] Very good evening to you and welcome again to a very special sports presentation here on SBS. It's Parry Roubaix night, the world's most anticipated one-day cycle race. Thank you so much for joining us for what promises to be a night of drama, a night of theatre, passion like no other.


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[Assistant] And let me remind you, it's not just a bike race, it is the most unique sporting event. And it's so good to have you along right here on SPSN. If you've been following the early part of the race via the Cycling Central live stream, stay with us for a brief preview.


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[Assistant] Because we will cross live to Northern France for the 2018 edition of the Hell of the North, the Queen of the Classics, very, very shortly.


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[Assistant] Well, one man who has lived and breathed Roubaix as a two-time top ten finisher is our very own Hank Vogels. He's with us in the studio yet again. Hank, it's Roubaix night.


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[Assistant] I think over the years we've seen riders, uh, race through, uh, horrible conditions, wet, uh, rain, mud, uh, but also dust and of course the sunny conditions, as was the case 11 years ago when, uh, Studio Grady won in 2007. You don't know what you're going to get, although it is quite sunny there today, but what makes it such a difficult race?


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[Assistant] Share it with us, let us in. Send your photos and comments on Twitter throughout the night by using the hashtag SBSCycling and we'll give you an update in just a moment. But the contenders, who's hot and who's not. There are many riders, it's probably the most unpredictable race and the names we'll throw up in a moment will probably indicate that.


[User] Please transcribe this text

[Assistant] Now he said that Peter, sorry, Tom Bonin, he said that Sargon should just shut up and if those around him are not going to help him, well, so be it. But this is the way Peter Sargon reacted or responded to Tom Bonin's tweet. He basically said, hey,


[User] Please transcribe this text

[Assistant] Oh, his career is certainly enjoying a second win. Houselough, as we say, was very strong last week and was in the mix at the end of the race. He is itching for another good result at Roubaix.


[User] Please transcribe this text

[Assistant] It's the riders you don't see during the course of the race, but the riders that are there at the finish that prove to be victorious, of course.


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[Assistant] And, uh, we'll look at the map now and, uh, it's an incredible course, 267 kilometres in total and, uh, they started from the town of, uh, Compiègne, the 116th edition.


[User] Please transcribe this text

[Assistant] They left a couple of hours ago now and, uh, headed into the north of France. They headed into hell. Uh, the course makes a beeline through this part of the country where riders must contest 29 cobbled sectors. And it's not until the forest of Ironberg when the race traditionally heats up.


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[Assistant] Hank, uh, like Flanders, Roubaix is a cobbled classic, uh, but without the punchy hills or the bergs.


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[Assistant] It presents a different challenge, I guess. Tell us, what are those challenges?


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[Assistant] I think I want to ask you about the Australian hope, uh, or the hopes, uh, there are six Australians competing and a handful of them of course for the Mitchelton Scott team. Some are saying the Aussies are champions in weighting. They made two changes from the team that competed in Flanders last week. Roger Kluger, the German, comes in.


[User] Please transcribe this text

[Assistant] Alright, it's party night around Australia. We're asking you to send some questions to Henk, to the team. And if you're lounging around next to your favourite person or with a group of friends, let us know where you are. We can now cross as they enter the third sector of 29 initiatives. Perry Reveille, let's now go to the commentary team. And tonight,


[User] Please transcribe this text

[Assistant] Ouch is definitely the word of uh, Paris Roubaix. So many crashes and I expect we'll see a lot more between now and the finish. That song was requested by Wednesday Legs Cycle and if you want to request the song to Troll DJ, do it right now. The hashtag is Troll DJ.


[User] Please transcribe this text

[Assistant] He or she is on standby waiting for your request. Hank, uh, we've gone through about three or four cobbled sectors now.


[User] Please transcribe this text

[Assistant] Tell us again what goes through the minds first of all of the riders as they hit these cobbles and secondly the bodies of the riders.


[User] Please transcribe this text

[Assistant] Allie from Lizmore, she's a good friend of us here at Cycling Central. She's asking, how do riders regain confidence on the road after a crash? There are many crashes as we've seen here in Parirubay. Allie says that she was hit by a driver while cycling to work and she's having trouble getting back in the saddle with confidence. Do these guys have the same problem?


[User] Please transcribe this text

[Assistant] But, uh, with 29 cobbled sectors, tell us the jarring effect that it has on the body. I can't imagine it.


[User] Please transcribe this text

[Assistant] A group of people there watching having a small little get together. Uh, the Bridge Road Brewery.


[User] Please transcribe this text

[Assistant] At Beechworth in Victoria. Uh, Two Monkeys at Pennshurst in Sydney. There's a Paris-Roubaix party going on there.


[User] Please transcribe this text

[Assistant] I've celebrated their 110th anniversary, the oldest club in Australia. And a big shout out also to Rippert Guinness.


[User] Please transcribe this text

[Assistant] Philip Joubert, uhm, he's appearing for the first time in 11 years. Joubert, uhm, why has he come back to a race that, you know, he's obviously better suited for the Ardennes club, well, one day races. Why do Paris-Roubaix


[User] Please transcribe this text

[Assistant] Hank, I'm really curious to see how, uh, the first timers perform.


[User] Please transcribe this text

[Assistant] Brenton, your young boy who has really improved out of sight over the last few years, puts on a great show and at least finishes


[User] Please transcribe this text

[Assistant] Uh, lifting this trophy is just surreal. Uhm, he's so happy that they are able to make it. Uhm, after this journey, MSI, they think the team, as a team, they grew so much, and they are more confident in facing any upcoming events in the future.


[User] Please transcribe this text

[Assistant] We're not that much of a better team than KZ. However, we're a more consistent team and we play cautiously. Whereas KZ, they're a bit anxious and they rush a bit and we're able to take their advantage.


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[Assistant] So he's just so grateful to have his teammates. Uh, he wasn't performing as well, uh, in earlier days in their scrims. But however, the team keeps trusting him, giving him the confidence to come back. So he's really grateful to have his teammates.


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[Assistant] So you'll definitely be one to watch tomorrow? Not necessarily one to watch, but uhm, this race is special. You have to like hide for as much as possible, uhm, and uh, if you do see me, it'll be right at the last moment.


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[Assistant] When I was a girl, I had a fear of spiders.


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[Assistant] I was told they felt no emotion. That their hearts never beat. But I know the truth.


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[Assistant] I was told they felt no emotion, that their hearts never beat. But I know the truth. At the moment of the kill, they are never more alive.


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[Assistant] Come on. It's time I showed you something.


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[Assistant] You took these because you wanted to see if there was more out there.


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[Assistant] Always remember, never accept the world as it appears to be. Dare to see it for what it could be.


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[Assistant] Never accept the world as it appears to be. Dare to see it for what it could be.


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[Assistant] Are you sure, Daddy Pig? Last time you got a bit grumpy when you dropped the pancake on the floor.


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[Assistant] When the news got round,


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[Assistant] We all rushed to see it. We had to queue for hours to get in.


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[Assistant] Those who failed to get in need not have felt disappointed.


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[Assistant] As many of the artists who should have appeared did not come. The only funny things we heard that evening


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[Assistant] Came from the advertiser at the beginning of the programme. He was obviously very nervous.


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[Assistant] And for some minutes stood awkwardly before the microphone. As soon as he opened his mouth, everyone burst out laughing.


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[Assistant] We all know what the poor man should have said, but what he actually said was,


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[Assistant] This is the Poo and E-Seed Bird Company. Good ladies, evening and gentlemen.


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[Assistant] Oh, she's here alright. Okay, you're in.


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[Assistant] You have no idea what it took for me to make this meeting happen.


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[Assistant] I mean, I'm the one that set off the alarm.


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[Assistant] Okay, listen. I'm here to make a friend. And show you something I found. Tell me.


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[Assistant] I don't let these images appear on every hall of it in the world. And you help out your new friend every now and then. What do you say? Clock's ticking, amiga.


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[Assistant] A few months ago in New York City, I had lunch with David and Wuhan, and over heaping plates of sushi, they outlined the themes of this 14th edition of Music at Menlo, Russian Reflections.


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[Assistant] And then they asked me if I might be able to provide an overview of the past thousand years or so.


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[Assistant] Of Russian musical history with a side trip to Paris to meet with Sergei Diaghilev and the Ballet Russes as a way of preparing and paving the way for a conversation about Russian composers in exile during the 20th century.


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[Assistant] I'm sure you know that David and Wuhan are authentic visionaries and they think big.


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[Assistant] And they know me rather well now, and I'm sure they were looking at me and thinking, he's just the right obsessive, compulsive job. And so I heard myself say, sure, fine, love to do it.


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[Assistant] And I began to realize that the challenge here was not going to be so much deciding which composers to include in the survey as which I was going to have to exclude in order to keep it merely to Russian novel length and not, uh,


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[Assistant] And we will move rather quickly up to, uh, Mikhail Glinka, who is often referred to as the father of the modern Russian school of music making.


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[Assistant] And along the way we will meet the so called mighty five or the mighty handful and we will leave off with Nikolai Rimsky-Korsakov and go have a ten minute break and see if we can find ourselves a little vodka.


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[Assistant] And when we come back, we will take that trip to Paris and there we will meet a little bit with Sergei Diaghilev, the ballet Rus'. And that will pave the way rather nicely for a more intensive conversation about Igor Stravinsky and the first three ballets that he wrote collaboratively with Diaghilev.


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[Assistant] Those would be the Firebird, Petrushka, and the Rite of Spring, and then the remaining


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[Assistant] 20 minutes or so of the conversation, we will meet with Sergey Prokofiev and Dmitry Shostakovich as representatives of the School of Russian Composers during the 20th century who were more directly impacted by Stalin and the Soviet Union. So lots to cover tonight.


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[Assistant] Since I've had to drop out some of my favorite Russian composers, I wanted to begin in the interest of completeness with an overview of all of the major Russian composers, and I found a rather good one in video form


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[Assistant] Uh, for those of you who don't know, Danny Kay's real name was David Daniel Kaminsky. So you've learned something already.


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[Assistant] In thinking about the history of Russian music, or for that matter, Russian art or literature or culture in general,


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[Assistant] The major issues always revolve around this complex interaction between Russia's Eastern and Western identities.


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[Assistant] Russia as we see in this picture of Russian Empire in 1914 has always been much too big. It's been too geographically and religiously and politically and racially diverse to be summed up as a single


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[Assistant] Peter the Great, and we have this cult of Saint Petersburg, his much-vaunted window on the West, which was his symbol of his desire to redraw Russia along Western European cultural lines.


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[Assistant] Then in the beginning of the 19th century we have this cult of the Slavophiles, the


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[Assistant] Many of the greatest writers and composers and artists of the day were those who found ways to reconcile or meld in convincing ways these elements of native Russian nationalism and international eclecticism.


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[Assistant] When you talk about Russian music and its history, it's worth noting that Russian music was a very late bloomer relative to Western European music.


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[Assistant] Music remained mired in medieval models from, uh, well really for hundreds of years largely because of this never-ending series of invasions from the Asian steppes, the Mongols, the Tartar hordes, and this level of warfare which was constantly going and kept them effectively isolated from, uh, Western European models of culture.


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[Assistant] It, uh, it was 988 that Vladimir the Great of Kiev consolidated Christianity along the Byzantine lines. And so for the last thousand years or so, Christianity in Russia has been Eastern Orthodox and not Roman Catholic.


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[Assistant] But as in Roman Catholic liturgy and services and ceremonies, there were hymns that were a part of the Eastern Orthodox ceremonies from medieval times. And rather than being called Gregorian chant, they were called Znameni chant.


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[Assistant] In the West, Gregorian chant developed rather naturally from medieval chant into Renaissance polyphony with additional voices interacting contrapuntally and then into Baroque magnificence and along the way we have the interpolation of instruments and instrumental accompaniment


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[Assistant] And of course, they tried to ban instrumental music entirely in Russia, but people being what they are, that was really impossible. And so,


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[Assistant] And they would sing these Russian folk songs and they would accompany themselves with tambourines or sometimes with these rather simple zithers which were called gusli.


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[Assistant] Anyway, this model of Russian zamenya chant and folk music sort of in secret on the side continued for hundreds of years and finally at the dawn of the 18th century you have Peter the Great taking over and


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[Assistant] European architects to come and lay it out on canals like Amsterdam.


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[Assistant] Not so much that he himself was a great dancer of the minuet. He was six foot eight tall. I don't know if you knew that. Enormous man.


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[Assistant] It was really his successors who took this craze for Western society and Western civilization to extremes. One of his successors was Empress Anna, and she, as we see in this picture, had this love of over-the-top entertainment, and it was she who


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[Assistant] Theatrical companies from Italy and also opera companies from Italy and then it was her successor


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[Assistant] Who took this love of extravagance and entertainment to pathological proportions. Uh, she was a very beautiful woman. She was the daughter of Peter the Great. She had about 15,000 of these gowns apparently. She liked to have massive parties, balls twice a week. Sometimes 800 people at a time.


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[Assistant] It was under her reign that the Winter Palace began to develop to its currently colossal proportions and she also began to really bring up music directors from Italy and elsewhere but particularly Italy.


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[Assistant] And it was one of them, a Neapolitan gentleman named Francesca Araya, who finally hit upon the idea of writing an Italian-style opera seria with Russian words. It was called Trefalis I Procris. And it became the first, quote, Russian opera. I'll play you a bit of the overture and you'll hear what it sounded like.


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[Assistant] It sounds like Italian 18th century Baroque music. It's lovely music, but it doesn't really sound very Russian.


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[Assistant] Her successor was Catherine the Great who had a 34 year reign and changed society quite a lot and she expanded this love of Italian opera exponentially and she brought up one Italian opera composer and


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[Assistant] Inductor and music director after another. Many of them didn't last very long because of the differences in the climate. They just kept fleeing back.


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[Assistant] ...became the mentor and the teacher of a Russian-born composer named Dmitry Bortnyansky, who was a very fine musician and was installed during Catherine's reign as the music director of the St. Petersburg Russian Chapel. He was the head of the, uh, of the religious component there.


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[Assistant] And his music doesn't sound at all like, uhm, that austere Damiani chant. It really sounds like Italian Baroque and pre-Baroque, uhm, liturgical music. They called it at one point the Russian Palestrina. And some of his music, some of his hymns, which are very beautiful,


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[Assistant] Have a tinge of Russian folk flavor to them. Probably the most famous is called Kol Slavin, which during the 19th century became the de facto anthem of the Russian Empire.


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[Assistant] And it was played on the Moscow carillon all the way up until 1917, uh, in October when the Bolshevik revolution happened and it made them stop. But I'll play you a little bit of it. It's, it's very beautiful and as I say, it has a tinge of Russian quality to it.


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[Assistant] Very beautiful music. It doesn't sound at all like that austere monophonic Znameni chant that we heard earlier. And at this time, as Znameni chant is going out of fashion and as the


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[Assistant] Church is losing its control over society. Uh, people began to become more and more interested in folk music, that music which had been developing in parallel to the Nam Yenny chant.


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[Assistant] In 1806, the Russian ambassador to the Habsburg court, whose name was Andrei Kirilevich Razumovsky, commissioned his friend Beethoven to write a set of three string quartets, which we know as the Razumovsky Quartets, Op. 59. And part of the deal was that


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[Assistant] Beethoven would interpolate Russian folk music into the fabric of these pieces. And so what you're looking at here is the tune that Beethoven extracted from this Lvov-Prach collection and based the fourth movement of opus 59, number one, upon.


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[Assistant] The major event, culturally, politically, of the early 19th century that sparked this nationalist Russian fervor that was predominant during the 19th century was of course the defeat of Napoleon and his French forces in 1812.


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[Assistant] And at this point, people began to really want to write Russian literature and Russian sounding art they wanted to paint and Russian music. And of course, the leading exponent of Russian literature was Pushkin, Alexander Pushkin.


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[Assistant] And he became an influence not only on literary figures of the next hundred years or so, but also on composers as well, partly because, you know, he was reaching down into his deep Russian soul and writing in Russian, and yet he had no compunction about reaching


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[Assistant] Byron and, and their influences are evident in his literature as well. And we see that in many of the composers, particularly that of a contemporary and friend of his named Mikhail Plinka.


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[Assistant] A little bit younger than Pushkin, but they came from the same upper crust level of the Lambda Gentry. And as such, they grew up in large rambling houses on country estates, worked by hordes of serfs.


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[Assistant] And he was, from an early age, uh, he became rather self-absorbed, and he became something of a hypochondriac, which is noted in this portrait of him by Ilya Repin, who is an artist we will hear a fair amount more from during the remaining portion of the first half of this, of this conversation.


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[Assistant] He developed a love of Russian folk music rather early in his life because he had an uncle who lived on a neighboring estate and he kept


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[Assistant] A private orchestra there and at mealtime in the evenings he would take the wind players and he would have them play arrangements of those Russian folk tunes that we just heard. And young Mikhail was often there and he fell in love with this music and he just, you know, grew to really identify with it at a very strong, basic level.


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[Assistant] When he was about 12 or 13 years old, he was sent off to an aristocratic boarding school in St. Petersburg. And there,


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[Assistant] Aside from going to school, he went to the opera quite a lot and he heard operas by great Russian composers of the day with names like Rossini and, uh, Carabini and Donizetti.


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[Assistant] And he decided that this is what he wanted to do. He wanted to become a composer himself. And his family said, forget it. I mean, this is beneath your station. You will become a member of the government bureaucracy. And so he did. And he hated it.


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[Assistant] And this is a pattern that we will see repeat over and over again among the 19th century Russian musical figures. They were mostly from the upper crust and they were mostly quashed in their desire to become musicians early in their lives.


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[Assistant] For almost three years, and during that time he reveled in Italian culture and food and particularly music.


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[Assistant] Which is to say that he didn't have this easy going, sunny, southern disposition. He was a northerner. He had this dark, Russian soul. And he wrote to a friend of his around that time and he defined


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[Assistant] His discovery of the nature of his soul this way, he said, with us Russians, things either make no impression at all, or they sink deeply into the soul.


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[Assistant] It is either a frenzy of joy or bitter tears.


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[Assistant] And so with this understanding, this epiphany about the nature of his soul, he left Italy.


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[Assistant] Went north, stopped off in Berlin for a number of months to study with Siegfried Dehn, who was the leading theorist of the day, polished up his knowledge of counterpoint, of harmony, of orchestration, and then his father died and he was called back to Russia.


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[Assistant] And by this time he was just on fire with the notion that he was going to write an indigenous Russian opera. And he chose as his subject the life of a early


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[Assistant] 17th century hero of Russian nationalism, Ivan Susunin, which was a very good choice because people knew this story and it dovetailed rather well with this Russian


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[Assistant] Fervor that was going on still in St. Petersburg. He wrote it. The name is now known as a life for the Tsar.


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[Assistant] And it was a huge hit. Uh, it catapulted him to fame both in Russia and in Western Europe. And he became known rather quickly as the composer of the first truly indigenous Russian opera. And I think one of the reasons that it was so popular is because it is essentially an Italian opera.


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[Assistant] It's got arias and it has choruses and it has dances and it's all woven together by this network of recitatives. And that's the foundation. But the superstructure is Russian folk music and Russian folk style. And it was very appealing to everybody. And to give you just a quick sample of it, a sense of what it sounded like, I'll play you a bit of the final


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[Assistant] Seeing here the, uh, the people are gathering in Red Square to celebrate the, the triumph of the new Tsar and they're singing, glory to you, Holy Rus.


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[Assistant] As you hear, it's very rousing music and you can understand why they liked it so much.


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[Assistant] And so with this great success under his belt, he decided to write a second opera, and he called it Ruslan and Ludmila.


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[Assistant] And he invited Pushkin to actually collaborate with him on writing the libretto, and they got to work on it. And as they were working, Pushkin got involved in a duel with a Frenchman whom he thought was trying to seduce his wife.


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[Assistant] And the two of them shot each other and Pushkin, a couple of days later, died.


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[Assistant] And this was a terrible blow to his friend Glinka and he began to spiral downwards in his life and his marriage collapsed around that time and he pulled himself together and he finished the opera.


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[Assistant] Said all the Russian composers who followed Glinka, myself included.


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[Assistant] Continue to this day to borrow contrapuntal and harmonic combinations directly. The fact is...


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[Assistant] Glinka managed to concentrate in one short work what a dozen second-rate talents would only have invented with the whole expenditure of their powers.


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[Assistant] This particular painting of him which makes him look very formidable indeed was done again by Ilya Repin and it really was he who


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[Assistant] Music or literature or art that the best that you could create was second-rate European art. And he felt that in order to do that, one thing you had to do was to give up on


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[Assistant] European models and institutionalized forms of learning and technique and right from your instinct, from your deep Russian soul.


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[Assistant] Nationalities as well, that exoticism and orientalism were to be encouraged, and so you have pieces like Scheherazade by Rimsky-Korsakov coming out of this movement, or Tamara by Mili Balakharev, which is about a sorceress from the Caucasus Mountains.


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[Assistant] We remember him particularly because he's the one who codified and began to recruit around him this myth of the mighty five, of the mighty handful. These young men


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[Assistant] Who included Mili Balakarev and Alexander Borodin and Modest Mazorski and Cesar Cui and Rimsky-Korsakov.


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[Assistant] They all would gather together on Saturday evenings at his home and they would read forehand piano music and they would discuss literature and poetry and mostly the direction that music should go, uh, in the remainder of the 19th century.


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[Assistant] The most, uhm, influential of these gentlemen, and I think the first of his protégés was Mili Malakarev. And he was, uh, a protégé also of Glinka. Glinka thought of him as the second Glinka. He said, you know, you will be my successor. We don't hear a great deal of his music anymore. He was


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[Assistant] Impressed them, he dazzled them, he gathered them into his orbit. He was the central sun around the others, which the others orbited. And he communicated these precepts of Stasov, particularly this one that said, don't model yourself after Western European


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[Assistant] I'll play you a piece by him, a little snippet of it, which is probably the only piece we hear on a regular basis. If you go to virtuoso piano recitals, occasionally you will hear this piece called Islami. Anybody know it?


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[Assistant] Yeah, it, it, I choose it for a couple of reasons. One is because it has that exotic sort of orientalist quality that, that Stasov was, was promoting. And also because I think it's a good example of this dazzling virtuosity that seems to have so impressed the other members of The Mighty Five.


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[Assistant] They were like Glinka all from the upper classes and as we look at them you'll see over and over again I've made these little galleries of them at various points in their lives and most of them began as military officers and cadets and the first one of them to really be successful


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[Assistant] Other than as a musician was Alexander Bordedean.


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[Assistant] You live a rather short but action packed, jam packed life. Got a PhD in organic chemistry at the University of Heidelberg.


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[Assistant] And then he went back and he taught organic chemistry at the Academy of Surgery and Medicine in St. Petersburg and was very influential and I think people who specialize in aldehydes still remember him as an organic chemist. He also was a great supporter of women's rights.


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[Assistant] Before his time in that way, he thought that women should be educated as well as men. He wrote poetry.


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[Assistant] Of course, in the 1860s, he fell under the sway of Balakharov, who inculcated him into this love of Russian nationalism and exoticism. And I think of the five, although he considered himself to be an amateur composer, he said, I'm a weekend composer, I compose on Sundays, he had this


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[Assistant] Amazing gift for writing melodies that just stick in your mind forever and won't leave you alone. As an example of that, I'll play you


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[Assistant] Of the five, he was the only one who wrote any appreciable amount of successful chamber music. Uh, we remember him for his string quartets, which are quite beautiful. He was a cellist himself.


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[Assistant] But they did not really attract the approval of Stasov and Belakarev because they felt that they were much too European in their style. Of the five, the one who was


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[Assistant] The most prolific as a composer and probably the least talented as a composer was César Cuit, who has a French name because his father was a French military officer.


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[Assistant] And like everybody else in the five, he fell under Balakarev's sway in the 1860s and he wrote, uh, I think about 15 or 16 operas, most of which have been forgotten. He wrote a lot of songs and a lot of orchestral music. Again, most of it has sort of evaporated into the mists of time.


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[Assistant] He and Tchaikovsky seem not to have liked each other's music very much, and I glean this from the things that they said about each other and each other's music, for example.


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[Assistant] By nature, Cuy is more drawn toward light and piquently rhythmic French music, but the demands of the invincible band, i.e. the Mighty Five, compel him to do violence to his natural gifts.


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[Assistant] And as an example of what Tchaikovsky just said, I'll play you the song of the Mandarin, which I think is piquently French and essentially in Sibid.


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[Assistant] But of the five, I mean as so often happens, he was perhaps the least inspired composer, but he had by far the biggest mouth.


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[Assistant] He became the, a leading critic for the St. Petersburg Press and in that guise he wrote reams of


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[Assistant] Amazingly opinionated and gratuitously nasty reviews, and probably the most nasty of all, the one we remember, is the one that he wrote in 1897 at the time of the review of the First Symphony of Sergei Rachmaninoff.


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[Assistant] Which was apparently subjected to an absolutely hideous performance. I think Glazunov was conducting it. He was apparently quite drunk at the time, so it was not a stellar performance of it. But even so, uh, Kui wrote, if there were a conservatory in hell and


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[Assistant] Then he would have fulfilled his task brilliantly and would delight the inhabitants of hell.


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[Assistant] This apparently was so devastating to the young Rachmaninoff who was never entirely confident of his abilities that he stopped composing for almost three years and had to go to a hypnotist named Dr. Dahl to cure himself of


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[Assistant] The effects of this review, so he had that to thank Kui for.


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[Assistant] Which is really too bad because he became a severe alcoholic and the last six years of his life were just one prolonged alcoholic debauch. He had a very difficult life.


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[Assistant] He was enormously talented. He played piano from an early age. And perhaps of all the five, he's the one who took Stasov and Balakharov most seriously when they said


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[Assistant] Some of his music struck even his closest friends and colleagues as a little crude and maybe full of mistakes. Particularly his friend Nikolai Rimsky-Korsakov. They were roommates for a period and apparently


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[Assistant] They liked each other very much, but, uh, Rimsky-Korsakov, as we'll see in a few minutes, was the one to sort of break out of Balakarev's orbit and go off and, uh, learn about Western technique big time. And so when


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[Assistant] His friend Mazorsky would write a piece, he would clean it up. I mean, he would say, I'm going to correct the mistakes and the harmonies and I'm going to make the orchestration more glittering and alluring so that your pieces will actually have a life. And he did that with


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[Assistant] By the time I got to the Met in 1977, the first opera that I played was Boris Gudnov, and by that time they had gone back to using Mussorgsky's original orchestration, which is, I will admit, it's a little crude and it's a little elemental, but it has this tremendous power to it, and this craggy eloquence to it, and it really was doing


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[Assistant] The 20th century that Russian composers began to look back on him and say,


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[Assistant] Music making. And, uh, Shostakovich himself kept a portrait of Mussorgsky on his writing table as inspiration.


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[Assistant] He, as I say, fell under Stasov's influence. And Stasov, as I, you know, we saw that portrait of him by Ilya Repin, and we saw another one of Glinka by Ilya Repin. Repin was an artist who was a proponent of this school of


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[Assistant] Artistic realism, which took hold during the 19th century. And the artistic realists, they wanted to represent in completely unvarnished and unforgiving detail the lives of the people. And so this is a famous picture by him.


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[Assistant] And Mazursky wanted to write music and songs and portraits of, of people from various levels of society that would be in certain ways analogues to this, this painting. And he was very specific about that. He wrote to his friend Repin at one point and he said, it is the people I want to depict.


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[Assistant] Sleeping or waking, eating and drinking, I have them constantly in my mind's eye. Again and again they rise before me in all their reality. Huge, unvarnished, with no tinsel.


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[Assistant] And his songs really are, I think, very much analogs to portraits like this. There's one, for instance, that he wrote in 1867 and dedicated to Stasov himself, which is called Orzornik, which means ragamuffin.


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[Assistant] Has unpredictable rhythmic structures and uses meters which you wouldn't normally find in 19th century.


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[Assistant] I mean arc music. And I'll play you a little bit of the beginning of this piece and you'll see I've outlined where the metrical changes take place and as you'll see it begins in a fast 5-4 time.


[User] Please transcribe this text

[Assistant] And then after a while it has a bar of 6-4 and then back to 5-4 and then suddenly it's 12-4 and then suddenly it's 5-4. Again, it's very, uh, must have been sort of upsetting to those people who


[User] Please transcribe this text

[Assistant] weren't familiar with this kind of unpredictability in, in metrical structures.


[User] Please transcribe this text

[Assistant] Again, he paid a high price for this, this kind of uncompromising.


[User] Please transcribe this text

[Assistant] Underivative, unrelentingly original music. He had a hard life. At a certain point his family lost their money when the serfs were freed in the 1860s. He went to work for the bureaucracy and worked at a rather menial job for 18 years and his drinking got worse and worse.