In recent years , deep neural networks have been utilized in a wide variety of applications including image generation . In particular , generative adversarial networks ( GANs ) are able to produce highly realistic pictures as part of tasks such as image compression . As with standard compression , it is desirable to be able to automatically assess the perceptual quality of generative images to monitor and control the encode process . Meanwhile , we observe the behavior of the classifier for various sized datasets with and without data augmentation . We observe that especially in datasets of certain sizes generative adversarial networks can be effectively used for data augmentation . Existing approaches for synthetic document generation rely on heuristics , or 2D and 3D geometric transformation-functions and are typically targeted at degrading the document . We tackle the problem of document synthesis and propose to train a particular form of Generative Adversarial Neural Networks , to learn a mapping function from an input image to an output image . With several experiments , we show that our algorithm generates an artificial historical document image that looks like a real historical document – for expert and non-expert eyes – by transferring the " historical style " to the classical electronic document . subsection : Generative Adversarial Network Generative adversarial networks have two components : a generator and a discriminator . For the generator , we follow the settings in . This is in line with the theorem in . When analyzing the convergence of generative adversarial nets , an important assumption is that the discriminator is allowed to reach its optimum given . Only if the discriminator is capable of differentiating real data from unnatural data consistently , the supervised signal from it can be meaningful and the whole adversarial training process can be stable and effective . Even for the identity - related attribute ( e . g . , gender ) , our DIAT can obtain visually impressive results by changing the attribute while retaining most identity - aware features . Facial attribute transfer , generative adversarial nets , convolutional networks , perceptual loss . section : Introduction Keywords : Generative Adversarial Nets ( GANs ) , Lipschitz regularity , Minimum Reconstruction Error ( MRE ) section : Introduction With $ f$-divergence as the objective function , the discriminator essentially estimates the density ratio , and the estimated ratio proves useful in further improving the sample quality of the generator . However , how to leverage the information contained in the discriminator of Wasserstein GANs ( WGAN ) is less explored . In this paper , we introduce the Discriminator Contrastive Divergence , which is well motivated by the property of WGAN 's discriminator and the relationship between WGAN and energy-based model . Wasserstein GANs are increasingly used in Computer Vision applications as they are easier to train . Previous WGAN variants mainly use the $ l_1 $ transport cost to compute the Wasserstein distance between the real and synthetic data distributions . The $ l_1 $ transport cost restricts the discriminator to be 1-Lipschitz . Qualitative and quantitative results on the CelebA , CelebA-HQ , LSUN and the ImageNet dog datasets show that WGAN-QC is better than state-of-art GAN methods . WGAN-QC has much faster runtime than other WGAN variants . We have also proposed a new way of measuring GAN quality which is based on image completion tasks . We have evaluated our method on four visual datasets : CelebA , LSUN Bedroom , CIFAR-10 , and 50K-SSFF , and have seen both visual and quantitative improvement over baseline WGAN . But we found in practice that gradient penalty WGANs ( GP-WGANs ) still suffer from training instability . In this paper , we combine a Total Variational ( TV ) regularizing term into the WGAN formulation instead of weight clipping or gradient penalty , which implies that the Lipschitz constraint is enforced on the critic network . Our proposed method is more stable at training than GP-WGANs and works well across varied GAN architectures . This paper presents a novel approach , end-to-end models , to simulate the desired images from the SAR image database . A series of generative adversarial networks include DCGAN , weight clipping WGAN and WGAN with gradient penalty are optimized and applied to generate typical SAR target images . Three kinds of network structures are used , include structure of DCGAN , newly proposed structure of four residual blocks networks and Resnet . However , exploiting DCNN for semantic segmentation tasks still faces significant challenges . In this thesis , a conditional Wasserstein generative adversarial network ( WGAN ) with gradient penalty is proposed to improve the generalization property of DCNN in order to achieve better accuracy . Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to their progressive down-sampling . This paper proposes a WGAN-empowered deep Autoencoder-based Outlier detection approach ( GAEO for short ) which presents a novel way to directly and effectively model reliable normal data . GAEO first obtains initial normal data by a proposed initial distance-based outlier scoring function , then constructs a WGAN network to perform normal data augmentation to obtain adequate training samples for subsequent normal data modelling . The deep autoencoder is then trained to discover and yield distributions and patterns of normal data , and the reconstruction error is defined as outlierness of each data object . In this paper , to solve the blurriness issue , we first theoretically analyze the cause of the blurriness issue . Subsequently , an image denoising training framework with WGAN based adversarial learning is proposed . This training framework solves the blurriness issue by guiding the denoising network to find the distribution space of real clean images rather than the distribution space of blurry images and introducing feature information . The classic GAN algorithm is difficult to train a model , and the improved Wasserstein GAN algorithm can make the model training more stable . Based on SRGAN model , this algorithm replaces the classical GAN algorithm with the improved WGAN algorithm . We will use the FY-3D satellite ’s Medium Resolution Spectral Imager Type II ( MERSI-II ) data , using super-resolution algorithms to make the reconstructed image significantly better visually . We study the ability of Wasserstein Generative Adversarial Network ( WGAN ) to generate missing audio content which is , in context , ( statistically similar ) to the sound and the neighboring borders . We deal with the challenge of audio inpainting long range gaps ( 500 ms ) using WGAN models . We improved the quality of the inpainting part using a new proposed WGAN architecture that uses a short-range and a long-range neighboring borders compared to the classical WGAN model . To show the claim for Indeed , if we put a box constraint on ψ [ reference ] or regularize the gradient of the critic function [ reference ] , the resulting MMD GAN generally matches or outperforms WGAN - based models . Unfortunately , though , an additive gradient penalty does n't substantially change the vector field of Figure 1 ( a ) , as shown in Figure 5 ( Appendix B ) . along with a vanishing boundary condition on f to ensure a well - defined solution ( although this was not used in the implementation , and can cause very unintuitive critic behavior ; see Appendix C ) . The authors considered several choices of µ , including the WGAN - GP measure [ reference ] and mixtures ( P + Q θ ) / 2 . 2 / 2 ) , except for MMDGAN - GP which uses a mixture of RQ kernels as in [ reference ] . Increasing the output dimension of the critic or using a different kernel did n't substantially change the performance of our proposed method . We also consider SMMD with a linear top - level kernel , k ( x , y ) = φ ψ ( x ) φ ψ ( y ) ; because this becomes essentially identical to a WGAN ( Appendix E ) , we refer to this method as SWGAN . SMMD and SWGAN use It also trained faster , and consistently outperformed other methods over multiple initializations ( Figure 2 ( a ) ) . It is worth noting that SN - SWGAN far outperformed WGAN - GP on both datasets . Table 1b presents the scores for SMMDGAN and SN - SMMDGAN trained on ImageNet , and the scores of pre - trained models using BGAN [ reference ] and SN - GAN [ reference ] . [ reference ] To learn disentangled representations , information - theoretic extension of GAN is proposed by maximizing the mutual information between a subset of noise variables and the generated results . In , WGAN and WGAN - GP minimize the Wasserstein - 1 distance between the generated distribution and the real distribution to improve the stability of learning generator . In this work , we adopt the WGAN framework to learn our DIAT model , and further suggest adaptive perceptual loss for identity - aware transfer and perceptual regularization to suppress visual artifacts . subsection : CNN for face generation For the evaluation of the performance of GANs at image generation , we introduce the ‘ Fréchet Inception Distance ’ ’ ( FID ) which captures the similarity of generated images to real ones better than the Inception Score . In experiments , TTUR improves learning for DCGANs and Improved Wasserstein GANs ( WGAN - GP ) outperforming conventional GAN training on CelebA , CIFAR - 10 , SVHN , LSUN Bedrooms , and the One Billion Word Benchmark . section : Introduction In contrast , InfoGAN proposed another information - theoretic GAN to learn disentangled representations capturing various latent concepts and factors in generating samples . Most recently , propose to minimize the Earth - Mover distance between the density of generated samples and the true data density , and they show the resultant Wasserstein GAN ( WGAN ) can address the vanishing gradient problem that the classic GAN suffers . Besides the class of GANs , there exist other models that also attempt to generate natural images . The term ' super resolution ' in the context of this paper defines the post processing enhancement of medical images as opposed to ' high resolution ' which defines native image resolution acquired during the MR acquisition phase . We also compare the SRGAN to three other models : SRCNN , SRResNet , and Sparse Representation . While the SRGAN results do not have the best Peak Signal to Noise Ratio ( PSNR ) or Structural Similarity ( SSIM ) metrics , they are the visually most similar to the original HR images , as portrayed by the Mean Opinion Score ( MOS ) results . The proposed discriminator network is applied to the super-resolution generative adversarial network ( SRGAN ) , which is called a resolution-preserving SRGAN ( RPSRGAN ) . Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does , and consequently , RPSRGAN with the new content loss improves the average peak signal-to-noise ratio ( PSNR ) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4 , respectively . For deblurring , the visual appearance is also significantly improved , and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network . Through adversarial training , the GAN can implicitly learn the underlying structure inherent to the dynamics of financial series and acquire the capacity to generate scenarios that share many similarities to those seen in the historic time series . In this article , the authors propose a data augmentation technique using the Wasserstein GAN with gradient penalty and show how training deep models on synthetic data mitigates overfitting , improving their performance on test data when compared to models trained solely on real data . TOPICS : Statistical methods , simulations , big data/machine learning Key Findings • Generative adversarial networks can capture the complex dynamics that govern many financial assets and produce realistic synthetic scenarios based on historic data . The advent of multiple types of remote sensing data of subaerial and subaqueous sedimentary patterns provides new possibilities in this context . Here , we train a Wasserstein GAN using 20,000 multispectral satellite images of subsections of 40 modern river deltas . The generated output has three facies and a background facies , and all quantitative evaluation methods of the unconditional output show a close overlap between the model and training data distributions . and ∈∀x⁢S ( PX ), =⁢DθD* ( x ) 0 . For loss functions like minimax and Wasserstein , may be interpreted as how plausible a sample is real . Thus at equilibrium , it may be reasonable to assume all real and generated samples are equally plausible . [ reference ] . This could be because for some kernel classes , D Ψ MMD is stronger than Wasserstein or MMD . Example 1 Another area to explore is the geometry of these losses , as studied by Bottou et al . [ reference ] , who showed potential advantages of the Wasserstein geometry over the MMD . Their results , though , do not address any distances based on optimized kernels ; the new distances introduced here might have interesting geometry of their own . Learning is based on a stochastic gradient of the discriminator ’s loss function and a stochastic gradient of the generator ’s loss function . The loss functions and can be the original as introduced in Goodfellow et al . , its improved versions , or recently proposed losses for GANs like the Wasserstein GAN . Our setting is not restricted to min - max GANs , but is valid for all other , more general GANs for which the discriminator ’s loss function is not necessarily related to the generator ’s loss function . However , from a time , resource , engineering , and computational perspective , it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data , that is , massive multidimensional data . Therefore , this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics . Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics . First , we use LSTM nodes to flexibly transform the spatial-temporal graph of human-environment interactions into feed-forward differentiable feature coding , and innovatively propose the global node to integrate scene context information . Then , we capture the relative importance of global interactions on pedestrian trajectories through scaled dot product attention , and use recurrent sequence modeling and generative adversarial network architecture for common training , so as to generate reasonable pedestrian future trajectory distributions based on rich mixed features . Experiments on public data sets show that STG-GAN is superior to previous work in terms of accuracy , reasoning speed and rationality of trajectory prediction . This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neural networks . The generative adversarial network structure is adopted , whereby a discriminative and a generative model are trained concurrently in an adversarial manner . Different network architectures are examined , with the discriminative and generative models structured as deep subnetworks of hidden layers comprised of convolutional or recurrent computational units . The outcomes were evaluated via cross-validation in two different datasets ( Kulaga-Yoskovitz and Winterburn ) . The Generative Adversarial Network scheme was tested regarding different architectural and training aspects , including the usage of skip-connections and a combined loss function . The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient of 90.84 % after the combined training of generator and discriminator with a joined loss function . Aiming at the problem that the QR codes can not be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product , this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur . Compared with the traditional deblurring algorithm , this method eliminates the process of blur kernel estimation , directly restores the motion blurred image . In this work , we introduce novel training losses that help recover fine details . We also present a multi-class GAN that can process multi-class image restoration tasks , i.e. , face and text images , using a single generator network . Extensive experiments demonstrate that our method performs favorably against the state-of-the-art methods on both synthetic and real-world images at a lower computational cost . Though there are Variational Auto Encoders ( VAEs ) for generating sketches in vector format , there is no Generative Adversarial Network ( GAN ) architecture for the same . In this paper , we propose a standalone GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN , for sketch generation in vector format . SkeGAN is a stochastic policy in Reinforcement Learning ( RL ) , capable of generating both multidimensional continuous and discrete outputs . The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak topology and can be optimized via gradient descent with relatively small batch sizes . In our evaluation on multiple benchmark datasets , including MNIST , CIFAR- 10 , CelebA and LSUN , the performance of MMD-GAN significantly outperforms GMMN , and is competitive with other representative GAN works . However , due to the scarcity of training samples in contrast to the complicated nature of medical image synthesis , learning a single global sample space mapping that is “ optimal ” to all samples is very challenging , if not intractable . To address this issue , this paper proposes sample-adaptive GAN models , which not only cater for the global sample space mapping between the source- and the target-modalities but also explore the local space around each given sample to extract its unique characteristic . Specifically , the proposed sample-adaptive GANs decompose the entire learning model into two cooperative paths . In this paper , we propose a Korean singing voice synthesis system that addresses the issues using an auto-regressive algorithm that generates spectrogram with the boundary equilibrium GAN objective . Through the qualitative test , we show the proposed methods are superior to the original GAN objective and non-auto-regressive model . We also show that our proposed method can render natural expressions such as continuous pitch contours and breath sounds . To prevent emergencies , a generative adversarial networks ( GAN ) model is designed and learns from the artificial emergencies from artificial traffic scenes . During deployment , the well-trained GAN model is used to generate multiple virtual emergencies based on the current real scene , and the well-trained planning model simultaneously makes different planning decisions for both virtual scenes and the current scenes . The final planning decision is made by comprehensively analyzing observations and virtual emergencies . Then , a mixing training strategy via a combination of GAN-based and classical data augmentation techniques is adopted to further improve recognition performance . The experimental results on two publicly available datasets demonstrate the effectiveness of our proposed GAN based data augmentation method in palmprint recognition . Our method is able to achieve 1.52 % and 0.37 % Equal Error Rates ( EER ) on IIT Delhi and CASIA palmprint datasets , respectively , which outperforms other existing methods . 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 . The generator ’s goal is to construct data in such a way that the discriminator can not tell them apart from real world data . In contrast , a generator which is overly fast , drives the discriminator steadily into new regions without capturing its gathered information . In recent GAN implementations , the discriminator often learned faster than the generator . A new objective slowed down the generator to prevent it from overtraining on the current discriminator . Unfortunately , it consists of a large number of constraints , whose scale is at an order of . This prevents us from using ( [ reference ] ) directly to solve an optimal non - parametric LS - GAN model with a very large number of training examples . On the contrary , a more tractable solution is to use a parameterized network to solve the optimization problem ( [ reference ] ) constrained in , and iteratively update parameterized and with the gradient descent method . In addition to the adversarial loss , we also introduce a conditional loss that imposes each generator to use the higher - level conditional information , and a novel entropy loss that encourages each generator to generate diverse representations . By stacking several GANs in a top - down way and using the top - most GAN to receive labels and the bottom - most GAN to generate images , SGAN can be trained to model the data distribution conditioned on class labels . Through extensive experiments , we demonstrate that our SGAN is able to generate images of much higher quality than a vanilla GAN . Generative adversarial nets ( GANs ) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions , given a limited computational budget . This study revisits MMD - GAN that uses the maximum mean discrepancy ( MMD ) as the loss function for GAN and makes two contributions . First , we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data . The details of the conditional GAN network architecture is shown in Table [ reference ] . Similarly to CoGAN learning , no correspondence was given during the conditional GAN learning . We applied the conditional GAN to the two digit generation tasks and hoped to answer whether a conditional model can be used to render corresponding images in two different domains without pairs of corresponding images in the training set . In order to model high - frequencies , it is sufficient to restrict our attention to the structure in local image patches . Therefore , we design a discriminator architecture – which we term a Patch GAN – that only penalizes structure at the scale of patches . This discriminator tries to classify if each patch in an image is real or fake .
In recent years , deep neural networks have been utilized in a wide variety of applications including image generation . In particular , generative adversarial networks ( GANs ) are able to produce highly realistic pictures as part of tasks such as image compression . As with standard compression , it is desirable to be able to automatically assess the perceptual quality of generative images to monitor and control the encode process .
Meanwhile , we observe the behavior of the classifier for various sized datasets with and without data augmentation . We observe that especially in datasets of certain sizes generative adversarial networks can be effectively used for data augmentation .
Existing approaches for synthetic document generation rely on heuristics , or 2D and 3D geometric transformation-functions and are typically targeted at degrading the document . We tackle the problem of document synthesis and propose to train a particular form of Generative Adversarial Neural Networks , to learn a mapping function from an input image to an output image . With several experiments , we show that our algorithm generates an artificial historical document image that looks like a real historical document – for expert and non-expert eyes – by transferring the " historical style " to the classical electronic document .
subsection : Generative Adversarial Network Generative adversarial networks have two components : a generator and a discriminator . For the generator , we follow the settings in .
This is in line with the theorem in . When analyzing the convergence of generative adversarial nets , an important assumption is that the discriminator is allowed to reach its optimum given . Only if the discriminator is capable of differentiating real data from unnatural data consistently , the supervised signal from it can be meaningful and the whole adversarial training process can be stable and effective .
Even for the identity - related attribute ( e . g . , gender ) , our DIAT can obtain visually impressive results by changing the attribute while retaining most identity - aware features . Facial attribute transfer , generative adversarial nets , convolutional networks , perceptual loss . section : Introduction
Keywords : Generative Adversarial Nets ( GANs ) , Lipschitz regularity , Minimum Reconstruction Error ( MRE ) section : Introduction
With $ f$-divergence as the objective function , the discriminator essentially estimates the density ratio , and the estimated ratio proves useful in further improving the sample quality of the generator . However , how to leverage the information contained in the discriminator of Wasserstein GANs ( WGAN ) is less explored . In this paper , we introduce the Discriminator Contrastive Divergence , which is well motivated by the property of WGAN 's discriminator and the relationship between WGAN and energy-based model .
Wasserstein GANs are increasingly used in Computer Vision applications as they are easier to train . Previous WGAN variants mainly use the $ l_1 $ transport cost to compute the Wasserstein distance between the real and synthetic data distributions . The $ l_1 $ transport cost restricts the discriminator to be 1-Lipschitz .
Qualitative and quantitative results on the CelebA , CelebA-HQ , LSUN and the ImageNet dog datasets show that WGAN-QC is better than state-of-art GAN methods . WGAN-QC has much faster runtime than other WGAN variants .
We have also proposed a new way of measuring GAN quality which is based on image completion tasks . We have evaluated our method on four visual datasets : CelebA , LSUN Bedroom , CIFAR-10 , and 50K-SSFF , and have seen both visual and quantitative improvement over baseline WGAN .
But we found in practice that gradient penalty WGANs ( GP-WGANs ) still suffer from training instability . In this paper , we combine a Total Variational ( TV ) regularizing term into the WGAN formulation instead of weight clipping or gradient penalty , which implies that the Lipschitz constraint is enforced on the critic network . Our proposed method is more stable at training than GP-WGANs and works well across varied GAN architectures .
This paper presents a novel approach , end-to-end models , to simulate the desired images from the SAR image database . A series of generative adversarial networks include DCGAN , weight clipping WGAN and WGAN with gradient penalty are optimized and applied to generate typical SAR target images . Three kinds of network structures are used , include structure of DCGAN , newly proposed structure of four residual blocks networks and Resnet .
However , exploiting DCNN for semantic segmentation tasks still faces significant challenges . In this thesis , a conditional Wasserstein generative adversarial network ( WGAN ) with gradient penalty is proposed to improve the generalization property of DCNN in order to achieve better accuracy . Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to their progressive down-sampling .
This paper proposes a WGAN-empowered deep Autoencoder-based Outlier detection approach ( GAEO for short ) which presents a novel way to directly and effectively model reliable normal data . GAEO first obtains initial normal data by a proposed initial distance-based outlier scoring function , then constructs a WGAN network to perform normal data augmentation to obtain adequate training samples for subsequent normal data modelling . The deep autoencoder is then trained to discover and yield distributions and patterns of normal data , and the reconstruction error is defined as outlierness of each data object .
In this paper , to solve the blurriness issue , we first theoretically analyze the cause of the blurriness issue . Subsequently , an image denoising training framework with WGAN based adversarial learning is proposed . This training framework solves the blurriness issue by guiding the denoising network to find the distribution space of real clean images rather than the distribution space of blurry images and introducing feature information .
The classic GAN algorithm is difficult to train a model , and the improved Wasserstein GAN algorithm can make the model training more stable . Based on SRGAN model , this algorithm replaces the classical GAN algorithm with the improved WGAN algorithm . We will use the FY-3D satellite ’s Medium Resolution Spectral Imager Type II ( MERSI-II ) data , using super-resolution algorithms to make the reconstructed image significantly better visually .
We study the ability of Wasserstein Generative Adversarial Network ( WGAN ) to generate missing audio content which is , in context , ( statistically similar ) to the sound and the neighboring borders . We deal with the challenge of audio inpainting long range gaps ( 500 ms ) using WGAN models . We improved the quality of the inpainting part using a new proposed WGAN architecture that uses a short-range and a long-range neighboring borders compared to the classical WGAN model .
To show the claim for Indeed , if we put a box constraint on ψ [ reference ] or regularize the gradient of the critic function [ reference ] , the resulting MMD GAN generally matches or outperforms WGAN - based models . Unfortunately , though , an additive gradient penalty does n't substantially change the vector field of Figure 1 ( a ) , as shown in Figure 5 ( Appendix B ) .
along with a vanishing boundary condition on f to ensure a well - defined solution ( although this was not used in the implementation , and can cause very unintuitive critic behavior ; see Appendix C ) . The authors considered several choices of µ , including the WGAN - GP measure [ reference ] and mixtures ( P + Q θ ) / 2 .
2 / 2 ) , except for MMDGAN - GP which uses a mixture of RQ kernels as in [ reference ] . Increasing the output dimension of the critic or using a different kernel did n't substantially change the performance of our proposed method . We also consider SMMD with a linear top - level kernel , k ( x , y ) = φ ψ ( x ) φ ψ ( y ) ; because this becomes essentially identical to a WGAN ( Appendix E ) , we refer to this method as SWGAN . SMMD and SWGAN use
It also trained faster , and consistently outperformed other methods over multiple initializations ( Figure 2 ( a ) ) . It is worth noting that SN - SWGAN far outperformed WGAN - GP on both datasets . Table 1b presents the scores for SMMDGAN and SN - SMMDGAN trained on ImageNet , and the scores of pre - trained models using BGAN [ reference ] and SN - GAN [ reference ] . [ reference ]
To learn disentangled representations , information - theoretic extension of GAN is proposed by maximizing the mutual information between a subset of noise variables and the generated results . In , WGAN and WGAN - GP minimize the Wasserstein - 1 distance between the generated distribution and the real distribution to improve the stability of learning generator . In this work , we adopt the WGAN framework to learn our DIAT model , and further suggest adaptive perceptual loss for identity - aware transfer and perceptual regularization to suppress visual artifacts . subsection : CNN for face generation
For the evaluation of the performance of GANs at image generation , we introduce the ‘ Fréchet Inception Distance ’ ’ ( FID ) which captures the similarity of generated images to real ones better than the Inception Score . In experiments , TTUR improves learning for DCGANs and Improved Wasserstein GANs ( WGAN - GP ) outperforming conventional GAN training on CelebA , CIFAR - 10 , SVHN , LSUN Bedrooms , and the One Billion Word Benchmark . section : Introduction
In contrast , InfoGAN proposed another information - theoretic GAN to learn disentangled representations capturing various latent concepts and factors in generating samples . Most recently , propose to minimize the Earth - Mover distance between the density of generated samples and the true data density , and they show the resultant Wasserstein GAN ( WGAN ) can address the vanishing gradient problem that the classic GAN suffers . Besides the class of GANs , there exist other models that also attempt to generate natural images .
The term ' super resolution ' in the context of this paper defines the post processing enhancement of medical images as opposed to ' high resolution ' which defines native image resolution acquired during the MR acquisition phase . We also compare the SRGAN to three other models : SRCNN , SRResNet , and Sparse Representation . While the SRGAN results do not have the best Peak Signal to Noise Ratio ( PSNR ) or Structural Similarity ( SSIM ) metrics , they are the visually most similar to the original HR images , as portrayed by the Mean Opinion Score ( MOS ) results .
The proposed discriminator network is applied to the super-resolution generative adversarial network ( SRGAN ) , which is called a resolution-preserving SRGAN ( RPSRGAN ) . Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does , and consequently , RPSRGAN with the new content loss improves the average peak signal-to-noise ratio ( PSNR ) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4 , respectively . For deblurring , the visual appearance is also significantly improved , and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network .
Through adversarial training , the GAN can implicitly learn the underlying structure inherent to the dynamics of financial series and acquire the capacity to generate scenarios that share many similarities to those seen in the historic time series . In this article , the authors propose a data augmentation technique using the Wasserstein GAN with gradient penalty and show how training deep models on synthetic data mitigates overfitting , improving their performance on test data when compared to models trained solely on real data . TOPICS : Statistical methods , simulations , big data/machine learning Key Findings • Generative adversarial networks can capture the complex dynamics that govern many financial assets and produce realistic synthetic scenarios based on historic data .
The advent of multiple types of remote sensing data of subaerial and subaqueous sedimentary patterns provides new possibilities in this context . Here , we train a Wasserstein GAN using 20,000 multispectral satellite images of subsections of 40 modern river deltas . The generated output has three facies and a background facies , and all quantitative evaluation methods of the unconditional output show a close overlap between the model and training data distributions .
and ∈∀x⁢S ( PX ), =⁢DθD* ( x ) 0 . For loss functions like minimax and Wasserstein , may be interpreted as how plausible a sample is real . Thus at equilibrium , it may be reasonable to assume all real and generated samples are equally plausible .
[ reference ] . This could be because for some kernel classes , D Ψ MMD is stronger than Wasserstein or MMD . Example 1
Another area to explore is the geometry of these losses , as studied by Bottou et al . [ reference ] , who showed potential advantages of the Wasserstein geometry over the MMD . Their results , though , do not address any distances based on optimized kernels ; the new distances introduced here might have interesting geometry of their own .
Learning is based on a stochastic gradient of the discriminator ’s loss function and a stochastic gradient of the generator ’s loss function . The loss functions and can be the original as introduced in Goodfellow et al . , its improved versions , or recently proposed losses for GANs like the Wasserstein GAN . Our setting is not restricted to min - max GANs , but is valid for all other , more general GANs for which the discriminator ’s loss function is not necessarily related to the generator ’s loss function .
However , from a time , resource , engineering , and computational perspective , it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data , that is , massive multidimensional data . Therefore , this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics . Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics .
First , we use LSTM nodes to flexibly transform the spatial-temporal graph of human-environment interactions into feed-forward differentiable feature coding , and innovatively propose the global node to integrate scene context information . Then , we capture the relative importance of global interactions on pedestrian trajectories through scaled dot product attention , and use recurrent sequence modeling and generative adversarial network architecture for common training , so as to generate reasonable pedestrian future trajectory distributions based on rich mixed features . Experiments on public data sets show that STG-GAN is superior to previous work in terms of accuracy , reasoning speed and rationality of trajectory prediction .
This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neural networks . The generative adversarial network structure is adopted , whereby a discriminative and a generative model are trained concurrently in an adversarial manner . Different network architectures are examined , with the discriminative and generative models structured as deep subnetworks of hidden layers comprised of convolutional or recurrent computational units .
The outcomes were evaluated via cross-validation in two different datasets ( Kulaga-Yoskovitz and Winterburn ) . The Generative Adversarial Network scheme was tested regarding different architectural and training aspects , including the usage of skip-connections and a combined loss function . The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient of 90.84 % after the combined training of generator and discriminator with a joined loss function .
Aiming at the problem that the QR codes can not be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product , this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur . Compared with the traditional deblurring algorithm , this method eliminates the process of blur kernel estimation , directly restores the motion blurred image .
In this work , we introduce novel training losses that help recover fine details . We also present a multi-class GAN that can process multi-class image restoration tasks , i.e. , face and text images , using a single generator network . Extensive experiments demonstrate that our method performs favorably against the state-of-the-art methods on both synthetic and real-world images at a lower computational cost .
Though there are Variational Auto Encoders ( VAEs ) for generating sketches in vector format , there is no Generative Adversarial Network ( GAN ) architecture for the same . In this paper , we propose a standalone GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN , for sketch generation in vector format . SkeGAN is a stochastic policy in Reinforcement Learning ( RL ) , capable of generating both multidimensional continuous and discrete outputs .
The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak topology and can be optimized via gradient descent with relatively small batch sizes . In our evaluation on multiple benchmark datasets , including MNIST , CIFAR- 10 , CelebA and LSUN , the performance of MMD-GAN significantly outperforms GMMN , and is competitive with other representative GAN works .
However , due to the scarcity of training samples in contrast to the complicated nature of medical image synthesis , learning a single global sample space mapping that is “ optimal ” to all samples is very challenging , if not intractable . To address this issue , this paper proposes sample-adaptive GAN models , which not only cater for the global sample space mapping between the source- and the target-modalities but also explore the local space around each given sample to extract its unique characteristic . Specifically , the proposed sample-adaptive GANs decompose the entire learning model into two cooperative paths .
In this paper , we propose a Korean singing voice synthesis system that addresses the issues using an auto-regressive algorithm that generates spectrogram with the boundary equilibrium GAN objective . Through the qualitative test , we show the proposed methods are superior to the original GAN objective and non-auto-regressive model . We also show that our proposed method can render natural expressions such as continuous pitch contours and breath sounds .
To prevent emergencies , a generative adversarial networks ( GAN ) model is designed and learns from the artificial emergencies from artificial traffic scenes . During deployment , the well-trained GAN model is used to generate multiple virtual emergencies based on the current real scene , and the well-trained planning model simultaneously makes different planning decisions for both virtual scenes and the current scenes . The final planning decision is made by comprehensively analyzing observations and virtual emergencies .
Then , a mixing training strategy via a combination of GAN-based and classical data augmentation techniques is adopted to further improve recognition performance . The experimental results on two publicly available datasets demonstrate the effectiveness of our proposed GAN based data augmentation method in palmprint recognition . Our method is able to achieve 1.52 % and 0.37 % Equal Error Rates ( EER ) on IIT Delhi and CASIA palmprint datasets , respectively , which outperforms other existing methods .
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 . The generator ’s goal is to construct data in such a way that the discriminator can not tell them apart from real world data .
In contrast , a generator which is overly fast , drives the discriminator steadily into new regions without capturing its gathered information . In recent GAN implementations , the discriminator often learned faster than the generator . A new objective slowed down the generator to prevent it from overtraining on the current discriminator .
Unfortunately , it consists of a large number of constraints , whose scale is at an order of . This prevents us from using ( [ reference ] ) directly to solve an optimal non - parametric LS - GAN model with a very large number of training examples . On the contrary , a more tractable solution is to use a parameterized network to solve the optimization problem ( [ reference ] ) constrained in , and iteratively update parameterized and with the gradient descent method .
In addition to the adversarial loss , we also introduce a conditional loss that imposes each generator to use the higher - level conditional information , and a novel entropy loss that encourages each generator to generate diverse representations . By stacking several GANs in a top - down way and using the top - most GAN to receive labels and the bottom - most GAN to generate images , SGAN can be trained to model the data distribution conditioned on class labels . Through extensive experiments , we demonstrate that our SGAN is able to generate images of much higher quality than a vanilla GAN .
Generative adversarial nets ( GANs ) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions , given a limited computational budget . This study revisits MMD - GAN that uses the maximum mean discrepancy ( MMD ) as the loss function for GAN and makes two contributions . First , we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data .
The details of the conditional GAN network architecture is shown in Table [ reference ] . Similarly to CoGAN learning , no correspondence was given during the conditional GAN learning . We applied the conditional GAN to the two digit generation tasks and hoped to answer whether a conditional model can be used to render corresponding images in two different domains without pairs of corresponding images in the training set .
In order to model high - frequencies , it is sufficient to restrict our attention to the structure in local image patches . Therefore , we design a discriminator architecture – which we term a Patch GAN – that only penalizes structure at the scale of patches . This discriminator tries to classify if each patch in an image is real or fake .
Quality Prediction on Deep Generative Images Experimental Assessment of the Performance of Data Augmentation with Generative Adversarial Networks in the Image Classification Problem Historical Document Synthesis with Generative Adversarial Networks Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Deep Identity-aware Transfer of Facial Attributes Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator Wasserstein GAN With Quadratic Transport Cost Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking A Wasserstein GAN model with the total variational regularization SAR Image Simulation by Generative Adversarial Networks Large-Scale 3-D Urban Mapping by Fusing InSAR and Optical Data An Outlier Detection Approach Based on WGAN-Empowered Deep Autoencoder Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement Super-Resolution Algorithm of Satellite Cloud Image Based on WGAN-GP Audio inpainting with generative adversarial network On gradient regularizers for MMD GANs Deep Identity-aware Transfer of Facial Attributes GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities An Application of Generative Adversarial Networks for Super Resolution Medical Imaging Resolution-Preserving Generative Adversarial Networks for Image Enhancement Mitigating Overfitting on Financial Datasets with Generative Adversarial Networks Geomodeling Using Generative Adversarial Networks and a Database of Satellite Imagery of Modern River Deltas Improving MMD-GAN Training with Repulsive Loss Function On gradient regularizers for MMD GANs GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings Multi-Modal Pedestrian Trajectory Prediction for Edge Agents Based on Spatial-Temporal Graph Generative adversarial networks for generation and classification of physical rehabilitation movement episodes Generative adversarial networks for automated hippocampus segmentation : development of an artificial neural network and integration in a generative adversarial network scheme to improve the segmentation of the hippocampus and its substructures Motion Deblur of QR Code Based on Generative Adversative Network Learning to Super-Resolve Blurry Face and Text Images Teaching GANs to Sketch in Vector Format MMD GAN: Towards Deeper Understanding of Moment Matching Network Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis Korean Singing Voice Synthesis Based on Auto-Regressive Boundary Equilibrium Gan Parallel Motion Planning: Learning a Deep Planning Model against Emergencies Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Stacked Generative Adversarial Networks Improving MMD-GAN Training with Repulsive Loss Function Coupled Generative Adversarial Networks Image-to-Image Translation with Conditional Adversarial Networks
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WML@ICDAR 2017 IEEE International Conference on Computer Vision (ICCV) AAAI ArXiv International Journal of Computer Vision ICCV IGARSS NeurIPS ArXiv NIPS International Journal of Computer Vision ICMLA ICLR NeurIPS NIPS ICCV ICASSP DICTA NIPS International Journal of Computer Vision 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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