More importantly , the endeavor has the potential to enrich our treasured collections of statistical models and expand the already vast reach of machine learning methodologies . Generative models enable us to learn useful features and representations from the natural images in an unsupervised manner . The learned features and representations can be more interpretable and explicit than those learned by the discriminative models , especially if the learned models are sparse . Consider a generative model defined as function $ g:{\cal U}^k\to{R}^n$ , $ { \cal U}\subset{R}$. Assume that the function $ g$ is trained such that it can describe a class of desired signals $ { \cal Q}\subset{R}^n$. The standard problem in noiseless compressed sensing is to recover $ { \bf x}\in{\cal Q}$ from under-determined linear measurements $ { \bf y}=A{\bf x}$ , where $ { \bf y}\in{R}^m$ and $ m\ll n$. A recovery method based on $ g$ finds $ g({\bf u})$ , $ \in{\cal U}^k$ , which has the minimum measurement error . In this paper , the performance of such a recovery method is studied and it is proven that , if the number of measurements ( $ m$ ) is larger than twice the dimension of the generative model ( $ k$ ) , then $ { \bf x}$ can be recovered from $ { \bf y}$ , with a distortion that is a function of the distortion induced by $ g$ in describing $ \bf x$ , i.e. $ \min_{{\bf u}\in{\cal U}^k}\|g({\bf u})-{\bf x}\|$. To derive an efficient method , an algorithm based on projected gradient descent is proposed . It is proven that , given enough measurements , the algorithm converges to the optimal solution and is robust to measurement noise . To analyze our observed distributions ( of number of calls , distinct call partners , and total talk time ) , we propose PowerTrack , a method which fits a lesser known but more suitable distribution , namely the Double Pareto LogNormal ( DPLN ) distribution , to our data and track its parameters over time . Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe . Furthermore , we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours . The book begins ( Chapter 1 ) with a treatment of what agent-based computational sociology is all about . Squazzoni makes his case for this kind of research , sketches the history of the field and elaborates on the major principles behind the general approach ( primacy of the model , generative perspective , micro – macro link , social processes ) . An outline of diverse ways how scholars use agent-based models follows naturally . Achieving good lossy compression while storing only high level latent variables would imply that representations learned at a high level contain information similar to that used by humans to judge images . As humans outperform the best machines at learning abstract representations , human evaluation of lossy compression obtained by these generative models constitutes a reasonable test of the quality of representations learned by these models . In the following we discuss variational auto - encoders and compression in more detail , present the algorithm and demonstrate the results on generation quality and compression . This observation not only underlines the need for automatic brain tumor segmentation tools , but also shows that research in that area is still a work in progress . Brain tumor segmentation methods ( especially those devoted to MRI ) can be roughly divided in two categories : those based on generative models and those based on discriminative models . Generative models rely heavily on domain - specific prior knowledge about the appearance of both healthy and tumorous tissues . We consider the question of how to assess generative adversarial networks , in particular with respect to whether or not they generalise beyond memorising the training data . We propose a simple procedure for assessing generative adversarial network performance based on a principled consideration of what the actual goal of generalisation is . Our approach involves using a test set to estimate the Wasserstein distance between the generative distribution produced by our procedure , and the underlying data distribution . Each of these two image modalities has its own advantages and disadvantages to visualize specimens , so biologists need these two complementary modalities together to analyze specimens . In this paper , we investigate a conditional Generative Adversarial Network ( conditional GAN ) , which contains one generator and two discriminators , to transfer microscopy image modalities . Given a training dataset consisting of pairs of images ( source and destination ) captured on the same set of specimens by DIC and Phase Contrast microscopes , we can train a conditional GAN , and with this well-trained GAN , we can generate the corresponding Phase Contrast image given a new DIC image , vice versa . Most of the state-of-the-art methods are highly dependent on the labelled training samples , which are difficult to collect in practical industrial applications . To address this problem , we propose a generative adversarial network based framework for unsupervised surface inspection . The generative adversarial network is trained to generate the fake images analogous to the normal surface images . StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples . In this paper we recap the StyleGAN architecture and training methodology and present our experiences of retraining it on a number of alternative public datasets . The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition . An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys , capable of learning the statistical properties of the seismic wavelets . The usage of validating and performance testing of compressive sensing are three steps . Unless the user has considerable artistic skill , it is easy to “ fall off ” the manifold of natural images while editing . In this paper , we propose to learn the natural image manifold directly from data using a generative adversarial neural network . We then define a class of image editing operations , and constrain their output to lie on that learned manifold at all times . In this paper , a new convolutional neural network model is presented for image completion . The proposed method is based on an auto-encoder and a Generative Adversarial Network structure . To succeed at this task and produce a plausible output for the damaged or distorted region(s ) , the auto-encoder part of the network needs to understand the content of the entire image . In addition , at a high compression ratio , the overall information , including details in the blocks , are lost owing to high quantization errors . We apply a generative adversarial network structure to restore these highly damaged blocks , and the results reveal that the image produced has details similar to those of the ground truth . Abstract Generative Adversarial Network ( GAN ) , a deep learning framework to generate synthetic but realistic samples , has produced astonishing results for image synthesis . However , because GAN is routinely used for image datasets , regularization methods for GAN have been developed for convolutional layers . [ reference ] applied a variational - autoencoder - based generative model to semisupervised learning . This work was followed by several variants [ reference ] . Generative adversarial networks ( GANs ) proposed by [ reference ] are a recently popular high - performance framework that can also be applied to semi - supervised learning [ reference ] , [ reference ] . In practice , these methods often require careful tuning of many hyperparameters in the generative model , and are usually not easy to implement without high expertise in its optimization process . They often suffer from regression - to - the - mean due to the ill - posed nature of single image SR , resulting in blurry predictions and poor subjective scores . To overcome this drawback , Generative Adversarial Networks have been used along with perceptual loss for SR . Subjective evaluation by mean - opinion - score showed huge improvement over other regression - based methods . document : Invertible Conditional GANs for image editing Generative Adversarial Networks ( GANs ) have recently demonstrated to successfully approximate complex data distributions . A relevant extension of this model is conditional GANs ( cGANs ) , where the introduction of external information allows to determine specific representations of the generated images . Li et al . suggest a CNN - based attribute transfer model from the optimization perspective , but both run time and transfer quality is far from satisfying . Considering that it is impracticable to collect labeled data for supervised learning , the generative adversarial net ( GAN ) framework usually is adopted for handling this task . However , visible artifacts and over - smoothing usually are inevitable in the transfer result for these methods . document : Loss - Sensitive Generative Adversarial Networks on Lipschitz Densities In this paper , we present the Lipschitz regularization theory and algorithms for a novel Loss - Sensitive Generative Adversarial Network ( LS - GAN ) . Specifically , it trains a loss function to distinguish between real and fake samples by designated margins , while learning a generator alternately to produce realistic samples by minimizing their losses . The recent boom of Machine Learning frameworks like Generative Adversarial Networks ( GAN ) , Deep Convolution Generative Adversarial Networks ( DCGAN ) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases . The clinical heterogeneity of one such chronic autoimmune disease like Systemic Lupus Erythematosus ( SLE ) , commonly referred to as Lupus , makes it difficult for medical diagnostics . In this work , for the first problem , we train an AutoEncoder with all the data ( labeled and unlabeled ) to obtain good features and introduce an active learning ( AL ) scheme to select and annotate a few most valuable samples from the unlabeled date set for model training . Then , we address the second problem by augmenting negative samples generated by a deep convolutional generative adversarial network ( DCGAN ) . Experiments on our dataset ( 738 Samples ) suggest the effectiveness of the proposed approach . The aspect of the humanoids and the humanness of interaction are key components of success . We developed a system to endow the humanoid robot Pepper , from SoftBank Robotics , with the capability of both : identify the emotion state of the humans and exhibiting emotional states via gestures and postures generated using a DCGAN model to learn from the human body language and to create originals body expressions to exhibit like-human movements . In this paper , we have made an in-depth exploration for the most popular DCGAN at present via utilizing TensorFlow deep learning framework , using the open CelebA face dataset of The Chinese University of Hong Kong as the data source . By comparing DCGAN unconstrained and DCGAN constrained , the experimental results show that the DCGAN model significantly improves the virtual face generation model after adding constraints in the training phase , which enhance the ability of the generator to deceive the discriminator . Finally , we have evaluated the proposed model from the perspective of TensorBoard and achieved the desired experimental results . The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching or mini-batch discrimination . By exploring an expressive posterior over the parameters of the generator , the Bayesian GAN avoids mode-collapse , produces interpretable and diverse candidate samples , and provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN , CelebA , and CIFAR-10 , outperforming DCGAN , Wasserstein GANs , and DCGAN ensembles . The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching , or mini-batch discrimination . By exploring an expressive posterior over the parameters of the generator , the Bayesian GAN avoids mode-collapse , produces interpretable candidate samples with notable variability , and in particular provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN , CelebA , and CIFAR-10 , outperforming DCGAN , Wasserstein GANs , and DCGAN ensembles . Here we present DeepScope , a generative , lightweight and real-time HCI platform for urban planning and cityscape visualization . DeepScope is composed of a Generative Neural Network ( DCGAN ) and a Tangible User Interface ( TUI ) designed for multi-participants urban design sessions and real-time feedback . In this paper we explore the design , development and deployment of the DeepScope platform , as well as discuss the potential implementation of DeepScope in urban design processes . First , an object location model is trained by DCNNs to obtain numerous samples of CSCs . Second , a generative model based on deep convolutional GAN ( DCGAN ) is constructed to find a good mapping from image space to high-dimensional feature spaces implicitly . Finally , an anomaly rating criterion is used to diagnose images . The task is not simply generating images randomly , but generating them conditionally , making a distinction according to the UI designates . To solve this task , we constructed the Conditional DCGAN model which includes the techniques from DCGAN and Conditional GAN . We tried training the models to be able to generate conditional images by adding label information as input to the Generator . The empirical results demonstrate that Using DCGAN , high-quality weather pictures can be generated on weather data sets . Additionally , after using DCGAN based data augmentation technology , our classification accuracy has improved . While DCGANs have been successful on natural images , we show its limited ability on font generation due to the high variation of fonts combined with the need of rigid structures of characters . We propose a class discriminative DCGAN which uses a classification network to work alongside the discriminative network to refine the generative network . This results of our experiment shows a dramatic improvement over the conventional DCGAN . The complex features of flow dynamics can be captured by the adversarial networks . The above DCGAN fluid model enables us to provide reasonable predictive accuracy of flow fields while maintaining a high computational efficiency . The performance of the DCGAN is illustrated for two test cases of Hokkaido tsunami with different incoming waves along the coastal line . We also apply an L1 weight regularization on the content representation with a weight of . We follow the procedure in DCGAN for training the model with adversarial loss . Datasets . For example , one of the most key components in the DCGAN is the batch normalization inserted between the fractional convolution layers in the generator network . It has been reported in literature that the batch normalization not only plays a key role in training the DCGAN model , but also prevents the mode collapse of the generator into few data points . The results were illustrated in Figure [ reference ] . For the sake of fair comparison , the conditional GAN shared the same architecture as the CLS - GAN . On the other hand , the DCGAN algorithm max - pooled the discriminator ’s convolution features from all layers to grids as the image features , and a L2 - SVM was then trained to classify images . The DCGAN was an unsupervised model which had shown competitive performance on generating photo - realistic images . Yet a scientific problem remains unknown : moving the generated samples out of the box and using them in currently available learning frameworks . To this end , this work uses unlabeled data produced by the DCGAN model in conjunction with the labeled training data . As shown in Fig . GAN training and testing . We use Tensorflow and the DCGAN package to train the GAN model using the provided data in the original training set without preprocessing ( , foreground detection ) . All the images are resized to and randomly flipped before training . In this paper , we propose an “ in vitro ” usage of the GANs for representation learning , , person re - identification . Using a baseline DCGAN model , we show that the imperfect GAN images effectively demonstrate their regularization ability when trained with a ResNet baseline model . Through the proposed LSRO method , we mix the unlabeled GAN images with the labeled real training images for simultaneous semi - supervised learning . This model also does not use label information . It can be viewed as a plain unconditional DCGAN model and serves as the ultimate baseline . We compare the generated samples ( Fig . As shown in Fig . [ reference ] ( d ) , DCGAN ( ) collapses to generating a single image per category , while adding the entropy loss enables it to generate diverse images ( Fig . [ reference ] This further demonstrates that entropy loss is effective at improving output diversity . The single DCGAN ( ) model obtains higher Inception score than the conditional DCGAN reported in . This suggests that might offer some advantages compared to a plain conditional DCGAN , even without stacking . For CIFAR - 10 , we used the CNN architecture proposed by [ reference ] with a 7 - layer critic and a 4 - layer generator . For CelebA , we used a 5 - layer DCGAN discriminator and a 10 - layer ResNet generator as in [ reference ] . For ImageNet , we used a 10 - layer ResNet for both the generator and discriminator . In all experiments we used 64 filters for the smallest convolutional layer , and double it at each layer ( CelebA / ImageNet ) or every other layer ( CIFAR - 10 ) . appendix : Extrapolation Inspired by the texture generation work by DBLP : conf / nips / GatysEB15 , theis2015generative and extrapolation test with DCGAN DBLP : journals / corr / RadfordMC15 , we also evaluate the statistics captured by our model by generating images twice or ten times as large as present in the dataset . The sequential deep learning algorithms usually can not finish the computation in an acceptable time . In this paper , we propose a many-core algorithm which is based on a parallel method and is used in the Intel Xeon Phi many-core systems to speed up the unsupervised training process of Sparse Autoencoder and Restricted Boltzmann Machine ( RBM ) . Using the sequential training algorithm as a baseline to compare , we adopted several optimization methods to parallelize the algorithm . The experimental results have demonstrated the effectiveness of the proposed methods for feature subset selection in high-dimensional feature space in terms of the number of selected features and the processing time spent to achieve the best classification accuracy . Third , a class-specific ( supervised ) pre-trained approach based on a sparse autoencoder is proposed for acquiring low-dimensional interesting structure of relevant features , which can be used for high-performance document classification . The experimental results have demonstrated the merit of this proposed method for document classification in high-dimensional feature space , in terms of the limited number of features required to achieve good classification accuracy . When applied to the MNIST and NORB datasets , we find that this method achieves better classification results than denoising autoencoders , networks trained with dropout , and RBMs . k-sparse autoencoders are simple to train and the encoding stage is very fast , making them well-suited to large problem sizes , where conventional sparse coding algorithms can not be applied . For example , the brain image needs to be divided accurately into non-enhancing tumour , enhancing tumour , tumour core and undamaged area . This paper utilizes four state-of-the-art convolution architectures to perform the segmentation of brain tumour , including the generative adversarial networks ( GANs ) , conditional deep convolution GANs , auto-encoders and u-nets . Based on adversarial networks , the author put forward a novel model for Image segmentation . Motivated by this , in this study we introduce ProstateGAN , a GAN-based model for synthesizing realistic prostate diffusion imaging data . More specifically , in order to generate new diffusion imaging data corresponding to a particular cancer grade ( Gleason score ) , we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process . Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores . It fails to capture strong geometric changes between domains , or it produces unsatisfactory results for complex scenes , compared to local texture mapping tasks such as style transfer . Recently , SAGAN ( Han Zhang , 2018 ) showed that the self-attention network produces better results than the convolution-based GAN . However , the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified . One interesting observation was that gains in the classification performance tend to transfer to significant quality gains in a wide variety of application domains . This means that architectural improvements in deep convolutional architecture can be utilized for improving performance for most other computer vision tasks that are increasingly reliant on high quality , learned visual features . Also , improvements in the network quality resulted in new application domains for convolutional networks in cases where AlexNet features could not compete with hand engineered , crafted solutions , e.g. proposal generation in detection . paragraph : DCGAN on Image Data . We test TTUR for the deep convolutional GAN ( DCGAN ) at the CelebA , CIFAR - 10 , SVHN and LSUN Bedrooms dataset . Fig . They often learn distributions with low support --- a phenomenon known as mode collapse --- and they do not guarantee the existence of a probability density , which makes evaluating generalization using predictive log-likelihood impossible . In this paper , we develop the prescribed GAN ( PresGAN ) to address these shortcomings . PresGANs add noise to the output of a density network and optimize an entropy-regularized adversarial loss .
More importantly , the endeavor has the potential to enrich our treasured collections of statistical models and expand the already vast reach of machine learning methodologies . Generative models enable us to learn useful features and representations from the natural images in an unsupervised manner . The learned features and representations can be more interpretable and explicit than those learned by the discriminative models , especially if the learned models are sparse .
Consider a generative model defined as function $ g:{\cal U}^k\to{R}^n$ , $ { \cal U}\subset{R}$. Assume that the function $ g$ is trained such that it can describe a class of desired signals $ { \cal Q}\subset{R}^n$. The standard problem in noiseless compressed sensing is to recover $ { \bf x}\in{\cal Q}$ from under-determined linear measurements $ { \bf y}=A{\bf x}$ , where $ { \bf y}\in{R}^m$ and $ m\ll n$. A recovery method based on $ g$ finds $ g({\bf u})$ , $ \in{\cal U}^k$ , which has the minimum measurement error . In this paper , the performance of such a recovery method is studied and it is proven that , if the number of measurements ( $ m$ ) is larger than twice the dimension of the generative model ( $ k$ ) , then $ { \bf x}$ can be recovered from $ { \bf y}$ , with a distortion that is a function of the distortion induced by $ g$ in describing $ \bf x$ , i.e. $ \min_{{\bf u}\in{\cal U}^k}\|g({\bf u})-{\bf x}\|$. To derive an efficient method , an algorithm based on projected gradient descent is proposed . It is proven that , given enough measurements , the algorithm converges to the optimal solution and is robust to measurement noise .
To analyze our observed distributions ( of number of calls , distinct call partners , and total talk time ) , we propose PowerTrack , a method which fits a lesser known but more suitable distribution , namely the Double Pareto LogNormal ( DPLN ) distribution , to our data and track its parameters over time . Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe . Furthermore , we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours .
The book begins ( Chapter 1 ) with a treatment of what agent-based computational sociology is all about . Squazzoni makes his case for this kind of research , sketches the history of the field and elaborates on the major principles behind the general approach ( primacy of the model , generative perspective , micro – macro link , social processes ) . An outline of diverse ways how scholars use agent-based models follows naturally .
Achieving good lossy compression while storing only high level latent variables would imply that representations learned at a high level contain information similar to that used by humans to judge images . As humans outperform the best machines at learning abstract representations , human evaluation of lossy compression obtained by these generative models constitutes a reasonable test of the quality of representations learned by these models . In the following we discuss variational auto - encoders and compression in more detail , present the algorithm and demonstrate the results on generation quality and compression .
This observation not only underlines the need for automatic brain tumor segmentation tools , but also shows that research in that area is still a work in progress . Brain tumor segmentation methods ( especially those devoted to MRI ) can be roughly divided in two categories : those based on generative models and those based on discriminative models . Generative models rely heavily on domain - specific prior knowledge about the appearance of both healthy and tumorous tissues .
We consider the question of how to assess generative adversarial networks , in particular with respect to whether or not they generalise beyond memorising the training data . We propose a simple procedure for assessing generative adversarial network performance based on a principled consideration of what the actual goal of generalisation is . Our approach involves using a test set to estimate the Wasserstein distance between the generative distribution produced by our procedure , and the underlying data distribution .
Each of these two image modalities has its own advantages and disadvantages to visualize specimens , so biologists need these two complementary modalities together to analyze specimens . In this paper , we investigate a conditional Generative Adversarial Network ( conditional GAN ) , which contains one generator and two discriminators , to transfer microscopy image modalities . Given a training dataset consisting of pairs of images ( source and destination ) captured on the same set of specimens by DIC and Phase Contrast microscopes , we can train a conditional GAN , and with this well-trained GAN , we can generate the corresponding Phase Contrast image given a new DIC image , vice versa .
Most of the state-of-the-art methods are highly dependent on the labelled training samples , which are difficult to collect in practical industrial applications . To address this problem , we propose a generative adversarial network based framework for unsupervised surface inspection . The generative adversarial network is trained to generate the fake images analogous to the normal surface images .
StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples . In this paper we recap the StyleGAN architecture and training methodology and present our experiences of retraining it on a number of alternative public datasets .
The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition . An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys , capable of learning the statistical properties of the seismic wavelets . The usage of validating and performance testing of compressive sensing are three steps .
Unless the user has considerable artistic skill , it is easy to “ fall off ” the manifold of natural images while editing . In this paper , we propose to learn the natural image manifold directly from data using a generative adversarial neural network . We then define a class of image editing operations , and constrain their output to lie on that learned manifold at all times .
In this paper , a new convolutional neural network model is presented for image completion . The proposed method is based on an auto-encoder and a Generative Adversarial Network structure . To succeed at this task and produce a plausible output for the damaged or distorted region(s ) , the auto-encoder part of the network needs to understand the content of the entire image .
In addition , at a high compression ratio , the overall information , including details in the blocks , are lost owing to high quantization errors . We apply a generative adversarial network structure to restore these highly damaged blocks , and the results reveal that the image produced has details similar to those of the ground truth .
Abstract Generative Adversarial Network ( GAN ) , a deep learning framework to generate synthetic but realistic samples , has produced astonishing results for image synthesis . However , because GAN is routinely used for image datasets , regularization methods for GAN have been developed for convolutional layers .
[ reference ] applied a variational - autoencoder - based generative model to semisupervised learning . This work was followed by several variants [ reference ] . Generative adversarial networks ( GANs ) proposed by [ reference ] are a recently popular high - performance framework that can also be applied to semi - supervised learning [ reference ] , [ reference ] . In practice , these methods often require careful tuning of many hyperparameters in the generative model , and are usually not easy to implement without high expertise in its optimization process .
They often suffer from regression - to - the - mean due to the ill - posed nature of single image SR , resulting in blurry predictions and poor subjective scores . To overcome this drawback , Generative Adversarial Networks have been used along with perceptual loss for SR . Subjective evaluation by mean - opinion - score showed huge improvement over other regression - based methods .
document : Invertible Conditional GANs for image editing Generative Adversarial Networks ( GANs ) have recently demonstrated to successfully approximate complex data distributions . A relevant extension of this model is conditional GANs ( cGANs ) , where the introduction of external information allows to determine specific representations of the generated images .
Li et al . suggest a CNN - based attribute transfer model from the optimization perspective , but both run time and transfer quality is far from satisfying . Considering that it is impracticable to collect labeled data for supervised learning , the generative adversarial net ( GAN ) framework usually is adopted for handling this task . However , visible artifacts and over - smoothing usually are inevitable in the transfer result for these methods .
document : Loss - Sensitive Generative Adversarial Networks on Lipschitz Densities In this paper , we present the Lipschitz regularization theory and algorithms for a novel Loss - Sensitive Generative Adversarial Network ( LS - GAN ) . Specifically , it trains a loss function to distinguish between real and fake samples by designated margins , while learning a generator alternately to produce realistic samples by minimizing their losses .
The recent boom of Machine Learning frameworks like Generative Adversarial Networks ( GAN ) , Deep Convolution Generative Adversarial Networks ( DCGAN ) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases . The clinical heterogeneity of one such chronic autoimmune disease like Systemic Lupus Erythematosus ( SLE ) , commonly referred to as Lupus , makes it difficult for medical diagnostics .
In this work , for the first problem , we train an AutoEncoder with all the data ( labeled and unlabeled ) to obtain good features and introduce an active learning ( AL ) scheme to select and annotate a few most valuable samples from the unlabeled date set for model training . Then , we address the second problem by augmenting negative samples generated by a deep convolutional generative adversarial network ( DCGAN ) . Experiments on our dataset ( 738 Samples ) suggest the effectiveness of the proposed approach .
The aspect of the humanoids and the humanness of interaction are key components of success . We developed a system to endow the humanoid robot Pepper , from SoftBank Robotics , with the capability of both : identify the emotion state of the humans and exhibiting emotional states via gestures and postures generated using a DCGAN model to learn from the human body language and to create originals body expressions to exhibit like-human movements .
In this paper , we have made an in-depth exploration for the most popular DCGAN at present via utilizing TensorFlow deep learning framework , using the open CelebA face dataset of The Chinese University of Hong Kong as the data source . By comparing DCGAN unconstrained and DCGAN constrained , the experimental results show that the DCGAN model significantly improves the virtual face generation model after adding constraints in the training phase , which enhance the ability of the generator to deceive the discriminator . Finally , we have evaluated the proposed model from the perspective of TensorBoard and achieved the desired experimental results .
The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching or mini-batch discrimination . By exploring an expressive posterior over the parameters of the generator , the Bayesian GAN avoids mode-collapse , produces interpretable and diverse candidate samples , and provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN , CelebA , and CIFAR-10 , outperforming DCGAN , Wasserstein GANs , and DCGAN ensembles .
The resulting approach is straightforward and obtains good performance without any standard interventions such as feature matching , or mini-batch discrimination . By exploring an expressive posterior over the parameters of the generator , the Bayesian GAN avoids mode-collapse , produces interpretable candidate samples with notable variability , and in particular provides state-of-the-art quantitative results for semi-supervised learning on benchmarks including SVHN , CelebA , and CIFAR-10 , outperforming DCGAN , Wasserstein GANs , and DCGAN ensembles .
Here we present DeepScope , a generative , lightweight and real-time HCI platform for urban planning and cityscape visualization . DeepScope is composed of a Generative Neural Network ( DCGAN ) and a Tangible User Interface ( TUI ) designed for multi-participants urban design sessions and real-time feedback . In this paper we explore the design , development and deployment of the DeepScope platform , as well as discuss the potential implementation of DeepScope in urban design processes .
First , an object location model is trained by DCNNs to obtain numerous samples of CSCs . Second , a generative model based on deep convolutional GAN ( DCGAN ) is constructed to find a good mapping from image space to high-dimensional feature spaces implicitly . Finally , an anomaly rating criterion is used to diagnose images .
The task is not simply generating images randomly , but generating them conditionally , making a distinction according to the UI designates . To solve this task , we constructed the Conditional DCGAN model which includes the techniques from DCGAN and Conditional GAN . We tried training the models to be able to generate conditional images by adding label information as input to the Generator .
The empirical results demonstrate that Using DCGAN , high-quality weather pictures can be generated on weather data sets . Additionally , after using DCGAN based data augmentation technology , our classification accuracy has improved .
While DCGANs have been successful on natural images , we show its limited ability on font generation due to the high variation of fonts combined with the need of rigid structures of characters . We propose a class discriminative DCGAN which uses a classification network to work alongside the discriminative network to refine the generative network . This results of our experiment shows a dramatic improvement over the conventional DCGAN .
The complex features of flow dynamics can be captured by the adversarial networks . The above DCGAN fluid model enables us to provide reasonable predictive accuracy of flow fields while maintaining a high computational efficiency . The performance of the DCGAN is illustrated for two test cases of Hokkaido tsunami with different incoming waves along the coastal line .
We also apply an L1 weight regularization on the content representation with a weight of . We follow the procedure in DCGAN for training the model with adversarial loss . Datasets .
For example , one of the most key components in the DCGAN is the batch normalization inserted between the fractional convolution layers in the generator network . It has been reported in literature that the batch normalization not only plays a key role in training the DCGAN model , but also prevents the mode collapse of the generator into few data points . The results were illustrated in Figure [ reference ] .
For the sake of fair comparison , the conditional GAN shared the same architecture as the CLS - GAN . On the other hand , the DCGAN algorithm max - pooled the discriminator ’s convolution features from all layers to grids as the image features , and a L2 - SVM was then trained to classify images . The DCGAN was an unsupervised model which had shown competitive performance on generating photo - realistic images .
Yet a scientific problem remains unknown : moving the generated samples out of the box and using them in currently available learning frameworks . To this end , this work uses unlabeled data produced by the DCGAN model in conjunction with the labeled training data . As shown in Fig .
GAN training and testing . We use Tensorflow and the DCGAN package to train the GAN model using the provided data in the original training set without preprocessing ( , foreground detection ) . All the images are resized to and randomly flipped before training .
In this paper , we propose an “ in vitro ” usage of the GANs for representation learning , , person re - identification . Using a baseline DCGAN model , we show that the imperfect GAN images effectively demonstrate their regularization ability when trained with a ResNet baseline model . Through the proposed LSRO method , we mix the unlabeled GAN images with the labeled real training images for simultaneous semi - supervised learning .
This model also does not use label information . It can be viewed as a plain unconditional DCGAN model and serves as the ultimate baseline . We compare the generated samples ( Fig .
As shown in Fig . [ reference ] ( d ) , DCGAN ( ) collapses to generating a single image per category , while adding the entropy loss enables it to generate diverse images ( Fig . [ reference ]
This further demonstrates that entropy loss is effective at improving output diversity . The single DCGAN ( ) model obtains higher Inception score than the conditional DCGAN reported in . This suggests that might offer some advantages compared to a plain conditional DCGAN , even without stacking .
For CIFAR - 10 , we used the CNN architecture proposed by [ reference ] with a 7 - layer critic and a 4 - layer generator . For CelebA , we used a 5 - layer DCGAN discriminator and a 10 - layer ResNet generator as in [ reference ] . For ImageNet , we used a 10 - layer ResNet for both the generator and discriminator . In all experiments we used 64 filters for the smallest convolutional layer , and double it at each layer ( CelebA / ImageNet ) or every other layer ( CIFAR - 10 ) .
appendix : Extrapolation Inspired by the texture generation work by DBLP : conf / nips / GatysEB15 , theis2015generative and extrapolation test with DCGAN DBLP : journals / corr / RadfordMC15 , we also evaluate the statistics captured by our model by generating images twice or ten times as large as present in the dataset .
The sequential deep learning algorithms usually can not finish the computation in an acceptable time . In this paper , we propose a many-core algorithm which is based on a parallel method and is used in the Intel Xeon Phi many-core systems to speed up the unsupervised training process of Sparse Autoencoder and Restricted Boltzmann Machine ( RBM ) . Using the sequential training algorithm as a baseline to compare , we adopted several optimization methods to parallelize the algorithm .
The experimental results have demonstrated the effectiveness of the proposed methods for feature subset selection in high-dimensional feature space in terms of the number of selected features and the processing time spent to achieve the best classification accuracy . Third , a class-specific ( supervised ) pre-trained approach based on a sparse autoencoder is proposed for acquiring low-dimensional interesting structure of relevant features , which can be used for high-performance document classification . The experimental results have demonstrated the merit of this proposed method for document classification in high-dimensional feature space , in terms of the limited number of features required to achieve good classification accuracy .
When applied to the MNIST and NORB datasets , we find that this method achieves better classification results than denoising autoencoders , networks trained with dropout , and RBMs . k-sparse autoencoders are simple to train and the encoding stage is very fast , making them well-suited to large problem sizes , where conventional sparse coding algorithms can not be applied .
For example , the brain image needs to be divided accurately into non-enhancing tumour , enhancing tumour , tumour core and undamaged area . This paper utilizes four state-of-the-art convolution architectures to perform the segmentation of brain tumour , including the generative adversarial networks ( GANs ) , conditional deep convolution GANs , auto-encoders and u-nets . Based on adversarial networks , the author put forward a novel model for Image segmentation .
Motivated by this , in this study we introduce ProstateGAN , a GAN-based model for synthesizing realistic prostate diffusion imaging data . More specifically , in order to generate new diffusion imaging data corresponding to a particular cancer grade ( Gleason score ) , we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process . Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores .
It fails to capture strong geometric changes between domains , or it produces unsatisfactory results for complex scenes , compared to local texture mapping tasks such as style transfer . Recently , SAGAN ( Han Zhang , 2018 ) showed that the self-attention network produces better results than the convolution-based GAN . However , the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified .
One interesting observation was that gains in the classification performance tend to transfer to significant quality gains in a wide variety of application domains . This means that architectural improvements in deep convolutional architecture can be utilized for improving performance for most other computer vision tasks that are increasingly reliant on high quality , learned visual features . Also , improvements in the network quality resulted in new application domains for convolutional networks in cases where AlexNet features could not compete with hand engineered , crafted solutions , e.g. proposal generation in detection .
paragraph : DCGAN on Image Data . We test TTUR for the deep convolutional GAN ( DCGAN ) at the CelebA , CIFAR - 10 , SVHN and LSUN Bedrooms dataset . Fig .
They often learn distributions with low support --- a phenomenon known as mode collapse --- and they do not guarantee the existence of a probability density , which makes evaluating generalization using predictive log-likelihood impossible . In this paper , we develop the prescribed GAN ( PresGAN ) to address these shortcomings . PresGANs add noise to the output of a density network and optimize an entropy-regularized adversarial loss .
Generative Modeling and Unsupervised Learning in Computer Vision Using auto-encoders for solving ill-posed linear inverse problems Mobile call graphs: beyond power-law and lognormal distributions Book Review: Agent-Based Computational Sociology Towards Conceptual Compression Brain tumor segmentation with Deep Neural Networks Towards a Testable Notion of Generalization for Generative Adversarial Networks Transferring Microscopy Image Modalities with Conditional Generative Adversarial Networks A Generative Adversarial Network Based Framework for Unsupervised Visual Surface Inspection Re-Training StyleGAN - A First Step Towards Building Large, Scalable Synthetic Facial Datasets Generative Inpainting Network Applications on Seismic Image Compression Generative Visual Manipulation on the Natural Image Manifold Image Completion with Discriminator Guided Context Encoder Local Excitation Network for Restoring a JPEG-Compressed Image Improved Recurrent Generative Adversarial Networks with Regularization Techniques and a Controllable Framework Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Image Super-Resolution via Dual-State Recurrent Networks Invertible Conditional GANs for image editing Deep Identity-aware Transfer of Facial Attributes Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Generative Adversarial Networks for Lupus Diagnostics Intelligent Glaucoma Diagnosis Via Active Learning And Adversarial Data Augmentation DCGAN Model Used To Generate Body Gestures On a Human-Humanoid Interaction System The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow Bayesian GAN Bayesian GANs DeepScope: HCI Platform for Generative Cityscape Visualization A Generic Anomaly Detection of Catenary Support Components Based on Generative Adversarial Networks Conditional DCGAN's Challenge: Generating Handwritten Character Digit, Alphabet and Katakana Imbalanced Adversarial Learning for Weather Image Generation and Classification Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network Diverse Image-to-Image Translation via Disentangled Representations Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Stacked Generative Adversarial Networks On gradient regularizers for MMD GANs Density estimation using Real NVP Training Large Scale Deep Neural Networks on the Intel Xeon Phi Many-Core Coprocessor Effective features and machine learning methods for document classification k-Sparse Autoencoders Brain Tumour Segmentation Using U-net Based Adversarial Networks ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks Unsupervised Image-to-Image Translation with Self-Attention Networks Rethinking the Inception Architecture for Computer Vision GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Prescribed Generative Adversarial Networks
9a9af914f81219917f5f38c6a8c11267a3aa966e 914d77c2efe7baf6773c2dcc78e130caf1813a01 6956feb91a2d4c11ef098ff501a203525f491254 ce78330df1eb3f371106f8e941c315b5660d9b00 ec7cca7197663f9e9cb5ca0bfa6c6c17f46c19a1 376141a4768d5d149ed6164a62b069e2e5d4e812 d3744325106b2c15b17786b3447bd55d874b54f8 5e0675694ccaed1beeae9607ab90935f412ba019 c38c1dede4005474066d3a21de4398e2b3f57fa6 fc7822f56dd255a872326b9536a0821bbf0277dd 252d65ee8d8f700fbe33f2c6ee104d678f67abfb eca7ef69251f82eff7d5cec044c98518d49aea54 814a00dd2f7e54cc30aec6bc280f86fdb5787768 7ddd204325c00834d65d8a5757532d93ad36598c 4acd83612c29983c6a82b9056b502e0a21056495 8724ab7c1fa353a807528f201f0bbf98ab03e98e 091a48ad0138482d4dcffd491200a2a66ad0ce6d 54161ff7a778668859aa246396a40860ef7f0e9f 0bc2962079e91c3fe561c55ef4e5cd0a23a37cfb 5818ea5bff27085bcd15b2d9b3c37755e8f339ff e63c0991e699f65d1e3d241c190a739439e490f7 6d73e21a16ef4f48fc0bc8717f10b6a44baf8a77 fb8771d702b566f9e7de3cf0452da5f597c1ff3a 591f66a9be968c6b0aad642f6e6dedb5d20e7fff 60edca5540ca484c7bf0b43c1141ace6eb12189c 4dfff93af2dbb8103784a41884c7d182b2288472 8db355b0ae6c59ca8c672f6c5844cb3a18b56316 b94043a133e3d07ed0b1cfc036829e619ea0ba22 da00575221371a2c94a5d8b8e89e07d53f78a9ae 3d23e3fdb28330dea6e68bdb59224218def0b5bc b1ddde52346f9c9858a3e7a0181c2ea3750a3ead fae3d474c4d7745be06458df0c20bf837a6055ef
["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Sociology"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Physics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"]
2016 2019 2008 2014 2016 2017 2018 2017 2018 2020 2019 2016 2018 2019 2020 2019 2018 2016 2016 2019 2019 2019 2019 2019 2017 2017 2020 2020 2019 2018 2017 2020 2018 2019 2017 2017 2018 2017 2014 2019 2013 2019 2018 2020 2016 2017 2019
KDD NIPS Medical Image Anal. CVPR Workshops ICASSP ECCV (5) ACSSC IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ArXiv International Journal of Computer Vision PEARC ISBI CHI Extended Abstracts ACPR ECCV International Journal of Computer Vision 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS ICLR IPDPS Workshops BigComp 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS
https://api.semanticscholar.org/9a9af914f81219917f5f38c6a8c11267a3aa966e https://api.semanticscholar.org/914d77c2efe7baf6773c2dcc78e130caf1813a01 https://api.semanticscholar.org/6956feb91a2d4c11ef098ff501a203525f491254 https://api.semanticscholar.org/ce78330df1eb3f371106f8e941c315b5660d9b00 https://www.semanticscholar.org/paper/bda89e0d181eda7e49ea831225eda86d075e111c https://www.semanticscholar.org/paper/441ff323c92331e655ce9ff896773fc00b55089a https://api.semanticscholar.org/ec7cca7197663f9e9cb5ca0bfa6c6c17f46c19a1 https://api.semanticscholar.org/376141a4768d5d149ed6164a62b069e2e5d4e812 https://api.semanticscholar.org/d3744325106b2c15b17786b3447bd55d874b54f8 https://api.semanticscholar.org/5e0675694ccaed1beeae9607ab90935f412ba019 https://api.semanticscholar.org/c38c1dede4005474066d3a21de4398e2b3f57fa6 https://api.semanticscholar.org/fc7822f56dd255a872326b9536a0821bbf0277dd https://api.semanticscholar.org/252d65ee8d8f700fbe33f2c6ee104d678f67abfb https://api.semanticscholar.org/eca7ef69251f82eff7d5cec044c98518d49aea54 https://api.semanticscholar.org/814a00dd2f7e54cc30aec6bc280f86fdb5787768 https://www.semanticscholar.org/paper/4b1c6f6521da545892f3f5dc39461584d4a27ec0 https://www.semanticscholar.org/paper/175f74a09241b6cb5101a2a09978095720db7d5f https://www.semanticscholar.org/paper/29afab1cb2d8ad8d37bd6f4a1adec42e131799fe https://www.semanticscholar.org/paper/2b507f659b341ed0f23106446de8e4322f4a3f7e https://www.semanticscholar.org/paper/4b71ab38d3ac6eee0da5e68ef666d33d1028bad8 https://api.semanticscholar.org/7ddd204325c00834d65d8a5757532d93ad36598c https://api.semanticscholar.org/4acd83612c29983c6a82b9056b502e0a21056495 https://api.semanticscholar.org/8724ab7c1fa353a807528f201f0bbf98ab03e98e https://api.semanticscholar.org/091a48ad0138482d4dcffd491200a2a66ad0ce6d https://api.semanticscholar.org/54161ff7a778668859aa246396a40860ef7f0e9f https://api.semanticscholar.org/0bc2962079e91c3fe561c55ef4e5cd0a23a37cfb https://api.semanticscholar.org/5818ea5bff27085bcd15b2d9b3c37755e8f339ff https://api.semanticscholar.org/e63c0991e699f65d1e3d241c190a739439e490f7 https://api.semanticscholar.org/6d73e21a16ef4f48fc0bc8717f10b6a44baf8a77 https://api.semanticscholar.org/fb8771d702b566f9e7de3cf0452da5f597c1ff3a https://api.semanticscholar.org/591f66a9be968c6b0aad642f6e6dedb5d20e7fff https://api.semanticscholar.org/60edca5540ca484c7bf0b43c1141ace6eb12189c https://www.semanticscholar.org/paper/3d5d9d8e74b215609eabba80ef79a35ebf460e49 https://www.semanticscholar.org/paper/4b71ab38d3ac6eee0da5e68ef666d33d1028bad8 https://www.semanticscholar.org/paper/f9b7783448f65205e085bd4e6fdfa2c8bfa9a4df https://www.semanticscholar.org/paper/9ec59d3b0320fc8abfaf297be45f0da6fdeef3d7 https://www.semanticscholar.org/paper/3d597e16ef56119bb6e6d78e3f379623862f0752 https://www.semanticscholar.org/paper/09879f7956dddc2a9328f5c1472feeb8402bcbcf https://api.semanticscholar.org/4dfff93af2dbb8103784a41884c7d182b2288472 https://api.semanticscholar.org/8db355b0ae6c59ca8c672f6c5844cb3a18b56316 https://api.semanticscholar.org/b94043a133e3d07ed0b1cfc036829e619ea0ba22 https://api.semanticscholar.org/da00575221371a2c94a5d8b8e89e07d53f78a9ae https://api.semanticscholar.org/3d23e3fdb28330dea6e68bdb59224218def0b5bc https://api.semanticscholar.org/b1ddde52346f9c9858a3e7a0181c2ea3750a3ead https://www.semanticscholar.org/paper/23ffaa0fe06eae05817f527a47ac3291077f9e58 https://www.semanticscholar.org/paper/231af7dc01a166cac3b5b01ca05778238f796e41 https://api.semanticscholar.org/fae3d474c4d7745be06458df0c20bf837a6055ef