The parameter-dependent part of the free-energy ( the difference between free-energy and posterior entropy ) is the essential entity in the derivation of learning algorithms . Here we show that for many common generative models the optimal values of the parameter-dependent part can be derived in closed-form . These closed-form expressions are hereby given as sums of the negative ( differential ) entropies of the individual model distributions . The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation . Based on a recently published generative model for visual inference ( George et al. , 2017 ) , we derive a family of anatomically instantiated and functional cortical circuit models . In contrast to simplistic models of Bayesian inference , the underlying generative model 's representational choices are validated with real-world tasks that required efficient inference and strong generalization . The multi-flow prediction is modeled in a variational probabilistic manner with spatial-temporal relationships learned through 3D convolutions . The flow-to-frame synthesis is modeled as a generative process in order to keep the predicted results lying closer to the manifold shape of real video sequence . Such a two-phase design prevents the model from directly looking at the high-dimensional pixel space of the frame sequence and is demonstrated to be more effective in predicting better and diverse results . In studies of grammaticalization , distinct , restricted , and therefore virtually predictable pathways of change ( as opposed to random parameter settings ) and evolving ( as opposed to innate ) grammatical categories have been identified as the linguistic/pragmatic outcomes of children ’s and adult speakers ’ use of language . In the generative perspective , language change is seen as resulting directly from the acquisition process , rather than from language usage . Evidence from grammaticalization studies makes it clear that understanding the nature of language development and change requires that actual usage , performance , be taken into account– not just competence . Beyond this , Choe and Charniak ’s generative variant of Vinyals et al . ( 2015 ) is another instance where generative models trained on the PTB outperform discriminative models . subsection : Ablated RNNGs GANs can learn complex generative models for which maximum likelihood or a variational approximations are infeasible . Instead of the likelihood , a discriminator network serves as objective for the generative model , that is , the generator . GAN learning is a game between the generator , which constructs synthetic data from random variables , and the discriminator , which separates synthetic data from real world data . In this paper , we have defined a class of invertible functions with tractable Jacobian determinant , enabling exact and tractable log - likelihood evaluation , inference , and sampling . We have shown that this class of generative model achieves competitive performances , both in terms of sample quality and log - likelihood . Many avenues exist to further improve the functional form of the transformations , for instance by exploiting the latest advances in dilated convolutions yu2015multi and residual networks architectures DBLP : journals / corr / TargAL16 . We show that TTN is superior to MPS for generative modeling in keeping correlation of pixels in natural images , as well as giving better log-likelihood scores in standard datasets of handwritten digits . We also compare its performance with state-of-the-art generative models such as the Variational AutoEncoders , Restricted Boltzmann machines , and PixelCNN . Finally , we discuss the future development of Tensor Network States in machine learning problems . Subsequently , we describe two new generative image models that exploit different image transformations as auxiliary variables : a quantized grayscale view of the image or a multi-resolution image pyramid . The proposed models tackle two known shortcomings of existing PixelCNN models : 1 ) their tendency to focus on low-level image details , while largely ignoring high-level image information , such as object shapes , and 2 ) their computationally costly procedure for image sampling . We experimentally demonstrate benefits of the proposed models , in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models . We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables . Subsequently , we describe two new generative image models that exploit different image transformations as latent variables : a quantized grayscale view of the image or a multi-resolution image pyramid . The proposed models tackle two known shortcomings of existing PixelCNN models : 1 ) their tendency to focus on low-level image details , while largely ignoring high-level image information , such as object shapes , and 2 ) their computationally costly procedure for image sampling . We experimentally demonstrate benefits of our LatentPixelCNN models , in particular showing that they produce much more realistically looking image samples than previous stateof-the-art probabilistic models . This paper proposed a novel visual tracking method based on the Gated PixelCNN and particle filtration . First , unlike the convolutional layers in traditional trackers , Gated PixelCNN is a generative model of images with a tractable likelihood . When trained , it is not limited to the existing classified datasets and video datasets . Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image . We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image . Then , given the generated low-resolution color image and the original grayscale image as inputs , we train a second CNN to generate a high-resolution colorization of an image . This technique , which we call a Variational Homoencoder ( VHE ) , produces a hierarchical latent variable model which better utilises latent variables . We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset , which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks . We additionally validate the VHE on natural images from the YouTube Faces database . The result is a more practical and general algorithm requiring no special apparatus . We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games . One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings , including Montezuma 's Revenge . Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced ( or equal ) number of parallel iterations . Experimentally , we demonstrate the effectiveness of our approach in accelerating 1 ) the evaluation of DenseNets on ImageNet and 2 ) autoregressive sampling of MADE and PixelCNN . We are able to achieve between 1.2 and 33 speedup factors under various conditions and computation models . In this paper , we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers . We significantly improve the state-of-the-art results on MNIST , OMNIGLOT , CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach . Interestingly , we also observe close to state-of-the-art results without the autoregressive part . Recent advances in modeling the distribution of natural images with neural networks allow them to generate increasingly natural - looking images . Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration . Following PixelRNN PixelRNN , we also model the color channels of the output pixels as discrete values generated from a multinomial distribution , implemented using a simple softmax layer . The current state of the art in modeling images on CIFAR - 10 data set was achieved by PixelCNN ++ , which models the output pixel distribution with a discretized logistic mixture likelihood , conditioning on whole pixels instead of color channels and changes to the architecture PixelCNNpp . These modifications are readily applicable to our model , which we plan to evaluate in future work . The proposed exploration bonus seems to be essential in games with very sparse rewards . We also compared our algorithm to DQN + CTS and DQN + PixelCNN . We can observe that , on average , DQN + SR outperforms these algorithms while being simpler since it does not require a density model . Conventional sparse , low rank , FRAME and non - local similarity based models usually are limited in capturing highly complex and long - range dependence between pixels . For better image modeling , a number of CNN - based methods have been proposed , including convolutional auto - encoder , PixelCNN and PixelRNN , and they have been applied to image completion and generation . Several CNN architectures have been developed for image generation . To work with the 12.5 ms frame hop of the spectrogram frames , only 2 upsampling layers are used in the conditioning stack instead of 3 layers . Instead of predicting discretized buckets with a softmax layer , we follow PixelCNN ++ and Parallel WaveNet and use a 10 - component mixture of logistic distributions ( MoL ) to generate 16 - bit samples at 24 kHz . To compute the logistic mixture distribution , the WaveNet stack output is passed through a ReLU activation followed by a linear projection to predict parameters ( mean , log scale , mixture weight ) for each mixture component . input_noise ] Appendix [ reference ] discusses the importance of the input noise in training PixelGAN autoencoders . The generative path is a conditional PixelCNN pixelcnn that conditions on the latent vector using an adaptive bias in PixelCNN layers . The inference is done by an amortized GAN inference technique that was originally proposed in the adversarial autoencoder work aae . Samples from and are provided to the adversarial network as the negative and positive examples respectively , and the generator of the adversarial network , which is also the encoder of the autoencoder , tries to match to by the gradient that comes through the discriminative adversarial network . The adversarial network , the PixelCNN decoder and the encoder are trained jointly in two phases – the reconstruction phase and the adversarial phase – executed on each mini - batch . In the reconstruction phase , the ground truth input along with the hidden code inferred by the encoder are provided to the PixelCNN decoder . For implementing the conditioning adaptive bias in the PixelCNN decoder , we explore two different architectures pixelcnn . In the location - invariant bias , for each PixelCNN layer , we use the latent code to construct a vector that is broadcasted within each feature map of the layer and then added as an adaptive bias to that layer . In the location - dependent bias , we use the latent code to construct a spatial feature map that is broadcasted across different feature maps and then added only to the first layer of the decoder as an adaptive bias . For the purpose of better illustrating the decomposition of information , we have chosen a 2 - D Gaussian latent code and a limited the receptive field of size 9 for the PixelGAN autoencoder . : mnist ] Figure [ reference ] b shows the samples of a PixelCNN model with the same limited receptive field size of 9 and : mnist ] Figure [ reference ] c shows the samples of an adversarial autoencoder with the 2 - D Gaussian latent code . This approach is very similar to our example of the PixelGAN autoencoder in : mnist ] Figure [ reference ] . However , the question that has remained unanswered is whether it is possible to achieve a complete decomposition of content and style in an unsupervised fashion , where the class label or discrete structure information is encoded in the latent code , and the remaining continuous structure such as style is captured by a powerful and deep PixelCNN decoder . This kind of decomposition is particularly interesting as it can be directly used for clustering and semi - supervised classification . section : Conclusion In this paper , we proposed the PixelGAN autoencoder , which is a generative autoencoder that combines a generative PixelCNN with a GAN inference network that can impose arbitrary priors on the latent code . We showed that imposing different distributions as the prior enables us to learn a latent representation that captures the type of statistics that we care about , while the remaining structure of the image is captured by the PixelCNN decoder . Generative adversarial networks ( GANs ) implicitly learn the probability distribution of a dataset and can draw samples from the distribution . This paper presents , Tabular GAN ( TGAN ) , a generative adversarial network which can generate tabular data like medical or educational records . Using the power of deep neural networks , TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables . When we evaluate our model on three datasets , we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets . TUL is usually studied as a multi-class classification problem and has gained recent attention because : ( 1 ) the number of labels/classes ( i.e. , users ) is way larger than the number of motion patterns among various trajectories ; and ( 2 ) the location-based trajectory data , especially the check-ins – i.e. , events of reporting a location at particular Point of Interest ( POI ) with known semantics – are often extremely sparse . Towards addressing these challenges , we introduce a Trajectory Generative Adversarial Network ( TGAN ) as an approach to enable learning users motion patterns and location distribution , and to eventually identify human mobility . TGAN consists of two jointly trained neural networks , playing a Minimax game to ( iteratively ) optimize both components . In this paper we propose a generative model , the Temporal Generative Adversarial Network ( TGAN ) , which can learn a semantic representation of unlabelled videos , and is capable of generating consistent videos . Unlike an existing GAN that generates videos with a generator consisting of 3D deconvolutional layers , our model exploits two types of generators : a temporal generator and an image generator . However , existing works experience limitations when generating large images ( the literature usually generates small images , e.g. 32 * 32 or 128 * 128 ) . In this paper , we propose a novel scheme , called deep tensor adversarial generative nets ( TGAN ) , that generates large high-quality images by exploring tensor structures . Essentially , the adversarial process of TGAN takes place in a tensor space . However , accurate localization is challenging due to issues such as real-time response requirements , limited fingerprint samples and mobile device storage . To address these problems , we propose a novel deep learning architecture : Tensor-Generative Adversarial Network ( TGAN ) . We first introduce a transform-based 3D tensor to model fingerprint samples . In this paper , we propose a generative model , Temporal Generative Adversarial Nets ( TGAN ) , which can learn a semantic representation of unlabeled videos , and is capable of generating videos . Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers , our model exploits two different types of generators : a temporal generator and an image generator . We describe a new initialization method for pixel based neural networks for object detection problems in which the locations of relatively small objects in large pictures must be found . The networks have a square input eld which is large enough to contain all objects of interest and are trained on examples which have been cut out from the large pictures . We introduce a two stage approach to the use of pixel based neural networks for finding relatively small objects in large pictures . In the first stage the network is trained by using a back propagation algorithm and a genetic algorithm on sample objects which have been cut out from the large pictures . In particular , it uses multi-temporal-spectral-spatial samples and deals with pixels contaminated by clouds/shadow present in multi-temporal data series . Using a Florida Everglades ecosystem study site covering an area of 771 square kilometers , the proposed PB-RNN system has achieved a significant improvement in the classification accuracy over a pixel-based recurrent neural network ( RNN ) system , a pixel-based single-image neural network ( NN ) system , a pixel-based multi-image NN system , a patch-based single-image NN system , and a patch-based multi-image NN system . For example , the proposed system achieves 97.21 % classification accuracy while the pixel-based single-image NN system achieves 64.74 % . Abstract . The paper considers the effectiveness of motion estimation in video using pixel-by-pixel recurrent algorithms . The algorithms use stochastic gradient decent to find inter-frame shifts of all pixels of a frame . This paper introduces an attention-guided channel to pixel convolution network for a fully automatic segmentation of retinal layers with choroidal neovascularization from optical coherence tomography ( OCT ) images . The proposed framework , consists of two new strategies for retinal layers segmentation : Channel to Pixel Block and Attention block . In traditional researches the form of the feature is usually so hand-crafted that effective feature is difficult to extract from skeletons . In this paper a unique method is proposed for human action recognition called Pixel Convolutional Networks , which use a natural and intuitive way to extract skeleton feature from two dimensions , space and time . It achieves good performance compared with mainstream methods in the past few years in the large dataset NTU-RGB+D. We demonstrate improved text-to-image synthesis with controllable object locations using an extension of Pixel Convolutional Neural Networks ( PixelCNN ) . In addition to conditioning on text , we show how the model can generate images conditioned on part keypoints and segmentation masks . Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images ( HSIs ) . In this paper , based on the mechanism of the population receptive field ( pRF ) in human visual cortex , we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network ( Pixel DAG-RNN ) to extract and apply spectral-spatial features for HSIs classification . In our model , an undirected cyclic graph ( UCG ) is used to represent the relevance connectivity of pixels in an image patch , and four DAGs are used to approximate the spatial relationship of UCGs . We discuss modern generative modelling of natural images based on neural networks . Three varieties of such models are particularly predominant at the time of writing : latent variable models such as variational autoencoders ( VAE ) , autoregressive models such as pixel recurrent neural networks ( PixelRNN ) , and generative adversarial networks ( GAN ) , which are noise-transformation models trained with an adversary . This thesis touches on all three kinds . The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring . A pixel-wise convolutional neural network ( CNN ) has demonstrated optimal performance in the classification of natural images by using abstracted deep features . Conventional CNNs demand a great deal of resources in terms of processing time and disk space . Experimentally we also find the discrete distribution to be easy to learn and to produce better performance compared to a continuous distribution ( Section [ reference ] ) . section : Pixel Recurrent Neural Networks In this section we describe the architectural components that compose the PixelRNN . In this paper , we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM , DBM , DBN , VAE and GAN . We will also take a look at three of state-of-the-art generative models , namely PixelRNN , DRAW and NADE . We will delve into their unique architectures , the learning procedures and their potential and limitations . The second type is the Diagonal BiLSTM layer where the convolution is applied in a novel fashion along the diagonals of the image . The networks also incorporate residual connections around LSTM layers ; we observe that this helps with training of the PixelRNN for up to twelve layers of depth . We also consider a second , simplified architecture which shares the same core components as the PixelRNN . Recent advances in modeling the distribution of natural images with neural networks allow them to generate increasingly natural - looking images . Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration . Conventional sparse , low rank , FRAME and non - local similarity based models usually are limited in capturing highly complex and long - range dependence between pixels . For better image modeling , a number of CNN - based methods have been proposed , including convolutional auto - encoder , PixelCNN and PixelRNN , and they have been applied to image completion and generation . Several CNN architectures have been developed for image generation .
The parameter-dependent part of the free-energy ( the difference between free-energy and posterior entropy ) is the essential entity in the derivation of learning algorithms . Here we show that for many common generative models the optimal values of the parameter-dependent part can be derived in closed-form . These closed-form expressions are hereby given as sums of the negative ( differential ) entropies of the individual model distributions .
The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation . Based on a recently published generative model for visual inference ( George et al. , 2017 ) , we derive a family of anatomically instantiated and functional cortical circuit models . In contrast to simplistic models of Bayesian inference , the underlying generative model 's representational choices are validated with real-world tasks that required efficient inference and strong generalization .
The multi-flow prediction is modeled in a variational probabilistic manner with spatial-temporal relationships learned through 3D convolutions . The flow-to-frame synthesis is modeled as a generative process in order to keep the predicted results lying closer to the manifold shape of real video sequence . Such a two-phase design prevents the model from directly looking at the high-dimensional pixel space of the frame sequence and is demonstrated to be more effective in predicting better and diverse results .
In studies of grammaticalization , distinct , restricted , and therefore virtually predictable pathways of change ( as opposed to random parameter settings ) and evolving ( as opposed to innate ) grammatical categories have been identified as the linguistic/pragmatic outcomes of children ’s and adult speakers ’ use of language . In the generative perspective , language change is seen as resulting directly from the acquisition process , rather than from language usage . Evidence from grammaticalization studies makes it clear that understanding the nature of language development and change requires that actual usage , performance , be taken into account– not just competence .
Beyond this , Choe and Charniak ’s generative variant of Vinyals et al . ( 2015 ) is another instance where generative models trained on the PTB outperform discriminative models . subsection : Ablated RNNGs
GANs can learn complex generative models for which maximum likelihood or a variational approximations are infeasible . Instead of the likelihood , a discriminator network serves as objective for the generative model , that is , the generator . GAN learning is a game between the generator , which constructs synthetic data from random variables , and the discriminator , which separates synthetic data from real world data .
In this paper , we have defined a class of invertible functions with tractable Jacobian determinant , enabling exact and tractable log - likelihood evaluation , inference , and sampling . We have shown that this class of generative model achieves competitive performances , both in terms of sample quality and log - likelihood . Many avenues exist to further improve the functional form of the transformations , for instance by exploiting the latest advances in dilated convolutions yu2015multi and residual networks architectures DBLP : journals / corr / TargAL16 .
We show that TTN is superior to MPS for generative modeling in keeping correlation of pixels in natural images , as well as giving better log-likelihood scores in standard datasets of handwritten digits . We also compare its performance with state-of-the-art generative models such as the Variational AutoEncoders , Restricted Boltzmann machines , and PixelCNN . Finally , we discuss the future development of Tensor Network States in machine learning problems .
Subsequently , we describe two new generative image models that exploit different image transformations as auxiliary variables : a quantized grayscale view of the image or a multi-resolution image pyramid . The proposed models tackle two known shortcomings of existing PixelCNN models : 1 ) their tendency to focus on low-level image details , while largely ignoring high-level image information , such as object shapes , and 2 ) their computationally costly procedure for image sampling . We experimentally demonstrate benefits of the proposed models , in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models .
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables . Subsequently , we describe two new generative image models that exploit different image transformations as latent variables : a quantized grayscale view of the image or a multi-resolution image pyramid . The proposed models tackle two known shortcomings of existing PixelCNN models : 1 ) their tendency to focus on low-level image details , while largely ignoring high-level image information , such as object shapes , and 2 ) their computationally costly procedure for image sampling . We experimentally demonstrate benefits of our LatentPixelCNN models , in particular showing that they produce much more realistically looking image samples than previous stateof-the-art probabilistic models .
This paper proposed a novel visual tracking method based on the Gated PixelCNN and particle filtration . First , unlike the convolutional layers in traditional trackers , Gated PixelCNN is a generative model of images with a tractable likelihood . When trained , it is not limited to the existing classified datasets and video datasets .
Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image . We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image . Then , given the generated low-resolution color image and the original grayscale image as inputs , we train a second CNN to generate a high-resolution colorization of an image .
This technique , which we call a Variational Homoencoder ( VHE ) , produces a hierarchical latent variable model which better utilises latent variables . We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset , which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks . We additionally validate the VHE on natural images from the YouTube Faces database .
The result is a more practical and general algorithm requiring no special apparatus . We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games . One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings , including Montezuma 's Revenge .
Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced ( or equal ) number of parallel iterations . Experimentally , we demonstrate the effectiveness of our approach in accelerating 1 ) the evaluation of DenseNets on ImageNet and 2 ) autoregressive sampling of MADE and PixelCNN . We are able to achieve between 1.2 and 33 speedup factors under various conditions and computation models .
In this paper , we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers . We significantly improve the state-of-the-art results on MNIST , OMNIGLOT , CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach . Interestingly , we also observe close to state-of-the-art results without the autoregressive part .
Recent advances in modeling the distribution of natural images with neural networks allow them to generate increasingly natural - looking images . Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration .
Following PixelRNN PixelRNN , we also model the color channels of the output pixels as discrete values generated from a multinomial distribution , implemented using a simple softmax layer . The current state of the art in modeling images on CIFAR - 10 data set was achieved by PixelCNN ++ , which models the output pixel distribution with a discretized logistic mixture likelihood , conditioning on whole pixels instead of color channels and changes to the architecture PixelCNNpp . These modifications are readily applicable to our model , which we plan to evaluate in future work .
The proposed exploration bonus seems to be essential in games with very sparse rewards . We also compared our algorithm to DQN + CTS and DQN + PixelCNN . We can observe that , on average , DQN + SR outperforms these algorithms while being simpler since it does not require a density model .
Conventional sparse , low rank , FRAME and non - local similarity based models usually are limited in capturing highly complex and long - range dependence between pixels . For better image modeling , a number of CNN - based methods have been proposed , including convolutional auto - encoder , PixelCNN and PixelRNN , and they have been applied to image completion and generation . Several CNN architectures have been developed for image generation .
To work with the 12.5 ms frame hop of the spectrogram frames , only 2 upsampling layers are used in the conditioning stack instead of 3 layers . Instead of predicting discretized buckets with a softmax layer , we follow PixelCNN ++ and Parallel WaveNet and use a 10 - component mixture of logistic distributions ( MoL ) to generate 16 - bit samples at 24 kHz . To compute the logistic mixture distribution , the WaveNet stack output is passed through a ReLU activation followed by a linear projection to predict parameters ( mean , log scale , mixture weight ) for each mixture component .
input_noise ] Appendix [ reference ] discusses the importance of the input noise in training PixelGAN autoencoders . The generative path is a conditional PixelCNN pixelcnn that conditions on the latent vector using an adaptive bias in PixelCNN layers . The inference is done by an amortized GAN inference technique that was originally proposed in the adversarial autoencoder work aae .
Samples from and are provided to the adversarial network as the negative and positive examples respectively , and the generator of the adversarial network , which is also the encoder of the autoencoder , tries to match to by the gradient that comes through the discriminative adversarial network . The adversarial network , the PixelCNN decoder and the encoder are trained jointly in two phases – the reconstruction phase and the adversarial phase – executed on each mini - batch . In the reconstruction phase , the ground truth input along with the hidden code inferred by the encoder are provided to the PixelCNN decoder .
For implementing the conditioning adaptive bias in the PixelCNN decoder , we explore two different architectures pixelcnn . In the location - invariant bias , for each PixelCNN layer , we use the latent code to construct a vector that is broadcasted within each feature map of the layer and then added as an adaptive bias to that layer . In the location - dependent bias , we use the latent code to construct a spatial feature map that is broadcasted across different feature maps and then added only to the first layer of the decoder as an adaptive bias .
For the purpose of better illustrating the decomposition of information , we have chosen a 2 - D Gaussian latent code and a limited the receptive field of size 9 for the PixelGAN autoencoder . : mnist ] Figure [ reference ] b shows the samples of a PixelCNN model with the same limited receptive field size of 9 and : mnist ] Figure [ reference ] c shows the samples of an adversarial autoencoder with the 2 - D Gaussian latent code .
This approach is very similar to our example of the PixelGAN autoencoder in : mnist ] Figure [ reference ] . However , the question that has remained unanswered is whether it is possible to achieve a complete decomposition of content and style in an unsupervised fashion , where the class label or discrete structure information is encoded in the latent code , and the remaining continuous structure such as style is captured by a powerful and deep PixelCNN decoder . This kind of decomposition is particularly interesting as it can be directly used for clustering and semi - supervised classification .
section : Conclusion In this paper , we proposed the PixelGAN autoencoder , which is a generative autoencoder that combines a generative PixelCNN with a GAN inference network that can impose arbitrary priors on the latent code . We showed that imposing different distributions as the prior enables us to learn a latent representation that captures the type of statistics that we care about , while the remaining structure of the image is captured by the PixelCNN decoder .
Generative adversarial networks ( GANs ) implicitly learn the probability distribution of a dataset and can draw samples from the distribution . This paper presents , Tabular GAN ( TGAN ) , a generative adversarial network which can generate tabular data like medical or educational records . Using the power of deep neural networks , TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables . When we evaluate our model on three datasets , we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets .
TUL is usually studied as a multi-class classification problem and has gained recent attention because : ( 1 ) the number of labels/classes ( i.e. , users ) is way larger than the number of motion patterns among various trajectories ; and ( 2 ) the location-based trajectory data , especially the check-ins – i.e. , events of reporting a location at particular Point of Interest ( POI ) with known semantics – are often extremely sparse . Towards addressing these challenges , we introduce a Trajectory Generative Adversarial Network ( TGAN ) as an approach to enable learning users motion patterns and location distribution , and to eventually identify human mobility . TGAN consists of two jointly trained neural networks , playing a Minimax game to ( iteratively ) optimize both components .
In this paper we propose a generative model , the Temporal Generative Adversarial Network ( TGAN ) , which can learn a semantic representation of unlabelled videos , and is capable of generating consistent videos . Unlike an existing GAN that generates videos with a generator consisting of 3D deconvolutional layers , our model exploits two types of generators : a temporal generator and an image generator .
However , existing works experience limitations when generating large images ( the literature usually generates small images , e.g. 32 * 32 or 128 * 128 ) . In this paper , we propose a novel scheme , called deep tensor adversarial generative nets ( TGAN ) , that generates large high-quality images by exploring tensor structures . Essentially , the adversarial process of TGAN takes place in a tensor space .
However , accurate localization is challenging due to issues such as real-time response requirements , limited fingerprint samples and mobile device storage . To address these problems , we propose a novel deep learning architecture : Tensor-Generative Adversarial Network ( TGAN ) . We first introduce a transform-based 3D tensor to model fingerprint samples .
In this paper , we propose a generative model , Temporal Generative Adversarial Nets ( TGAN ) , which can learn a semantic representation of unlabeled videos , and is capable of generating videos . Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers , our model exploits two different types of generators : a temporal generator and an image generator .
We describe a new initialization method for pixel based neural networks for object detection problems in which the locations of relatively small objects in large pictures must be found . The networks have a square input eld which is large enough to contain all objects of interest and are trained on examples which have been cut out from the large pictures .
We introduce a two stage approach to the use of pixel based neural networks for finding relatively small objects in large pictures . In the first stage the network is trained by using a back propagation algorithm and a genetic algorithm on sample objects which have been cut out from the large pictures .
In particular , it uses multi-temporal-spectral-spatial samples and deals with pixels contaminated by clouds/shadow present in multi-temporal data series . Using a Florida Everglades ecosystem study site covering an area of 771 square kilometers , the proposed PB-RNN system has achieved a significant improvement in the classification accuracy over a pixel-based recurrent neural network ( RNN ) system , a pixel-based single-image neural network ( NN ) system , a pixel-based multi-image NN system , a patch-based single-image NN system , and a patch-based multi-image NN system . For example , the proposed system achieves 97.21 % classification accuracy while the pixel-based single-image NN system achieves 64.74 % .
Abstract . The paper considers the effectiveness of motion estimation in video using pixel-by-pixel recurrent algorithms . The algorithms use stochastic gradient decent to find inter-frame shifts of all pixels of a frame .
This paper introduces an attention-guided channel to pixel convolution network for a fully automatic segmentation of retinal layers with choroidal neovascularization from optical coherence tomography ( OCT ) images . The proposed framework , consists of two new strategies for retinal layers segmentation : Channel to Pixel Block and Attention block .
In traditional researches the form of the feature is usually so hand-crafted that effective feature is difficult to extract from skeletons . In this paper a unique method is proposed for human action recognition called Pixel Convolutional Networks , which use a natural and intuitive way to extract skeleton feature from two dimensions , space and time . It achieves good performance compared with mainstream methods in the past few years in the large dataset NTU-RGB+D.
We demonstrate improved text-to-image synthesis with controllable object locations using an extension of Pixel Convolutional Neural Networks ( PixelCNN ) . In addition to conditioning on text , we show how the model can generate images conditioned on part keypoints and segmentation masks .
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images ( HSIs ) . In this paper , based on the mechanism of the population receptive field ( pRF ) in human visual cortex , we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network ( Pixel DAG-RNN ) to extract and apply spectral-spatial features for HSIs classification . In our model , an undirected cyclic graph ( UCG ) is used to represent the relevance connectivity of pixels in an image patch , and four DAGs are used to approximate the spatial relationship of UCGs .
We discuss modern generative modelling of natural images based on neural networks . Three varieties of such models are particularly predominant at the time of writing : latent variable models such as variational autoencoders ( VAE ) , autoregressive models such as pixel recurrent neural networks ( PixelRNN ) , and generative adversarial networks ( GAN ) , which are noise-transformation models trained with an adversary . This thesis touches on all three kinds .
The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring . A pixel-wise convolutional neural network ( CNN ) has demonstrated optimal performance in the classification of natural images by using abstracted deep features . Conventional CNNs demand a great deal of resources in terms of processing time and disk space .
Experimentally we also find the discrete distribution to be easy to learn and to produce better performance compared to a continuous distribution ( Section [ reference ] ) . section : Pixel Recurrent Neural Networks In this section we describe the architectural components that compose the PixelRNN .
In this paper , we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM , DBM , DBN , VAE and GAN . We will also take a look at three of state-of-the-art generative models , namely PixelRNN , DRAW and NADE . We will delve into their unique architectures , the learning procedures and their potential and limitations .
The second type is the Diagonal BiLSTM layer where the convolution is applied in a novel fashion along the diagonals of the image . The networks also incorporate residual connections around LSTM layers ; we observe that this helps with training of the PixelRNN for up to twelve layers of depth . We also consider a second , simplified architecture which shares the same core components as the PixelRNN .
Recent advances in modeling the distribution of natural images with neural networks allow them to generate increasingly natural - looking images . Some models , such as the PixelRNN and PixelCNN , have a tractable likelihood . Beyond licensing the comparatively simple and stable training regime of directly maximizing log - likelihood , this enables the straightforward application of these models in problems such as image compression and probabilistic planning and exploration .
Conventional sparse , low rank , FRAME and non - local similarity based models usually are limited in capturing highly complex and long - range dependence between pixels . For better image modeling , a number of CNN - based methods have been proposed , including convolutional auto - encoder , PixelCNN and PixelRNN , and they have been applied to image completion and generation . Several CNN architectures have been developed for image generation .
Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models Cortical Microcircuits from a Generative Vision Model Flow-Grounded Spatial-Temporal Video Prediction from Still Images The evolution of meaning and grammar What Do Recurrent Neural Network Grammars Learn About Syntax? GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Density estimation using Real NVP Tree Tensor Networks for Generative Modeling PixelCNN Models with Auxiliary Variables for Natural Image Modeling Latent Variable PixelCNNs for Natural Image Modeling Visual Tracking by Gated PixelCNN Model PixColor: Pixel Recursive Colorization The Variational Homoencoder: Learning to learn high capacity generative models from few examples Count-Based Exploration with Neural Density Models Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation Feature Map Variational Auto-Encoders Image Transformer Deep Identity-aware Transfer of Facial Attributes Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions PixelGAN Autoencoders Synthesizing Tabular Data using Generative Adversarial Networks Improving human mobility identification with trajectory augmentation Temporal Generative Adversarial Nets TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation Tensor-Generative Adversarial Network with Two-Dimensional Sparse Coding: Application to Real-Time Indoor Localization Temporal Generative Adversarial Nets with Singular Value Clipping Centred Weight Initialization in Neural Networks for Object Detection Using Back Propagation Algorithm and Genetic Algorithm to Train and Refine Neural Networks for Object Detection Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks PIXEL-BY-PIXEL ESTIMATION OF SCENE MOTION IN VIDEO Attention-guided channel to pixel convolution network for retinal layer segmentation with choroidal neovascularization Pixel Convolutional Networks for Skeleton-Based Human Action Recognition Generating Interpretable Images with Controllable Structure Pixel Dag-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification Generative models for natural images Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring Pixel Recurrent Neural Networks Deep generative models: Survey Pixel Recurrent Neural Networks Image Transformer Deep Identity-aware Transfer of Facial Attributes
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AISTATS ECCV (9) EACL NIPS ICLR CSAI ICML ArXiv 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) NIPS DEXA Medical Imaging: Image Processing ICML ICML ICML ArXiv
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