A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

[1]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[2]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[3]  Jürgen Schmidhuber,et al.  Learning Factorial Codes by Predictability Minimization , 1992, Neural Computation.

[4]  Brendan J. Frey,et al.  Does the Wake-sleep Algorithm Produce Good Density Estimators? , 1995, NIPS.

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[6]  Brendan J. Frey,et al.  Graphical Models for Machine Learning and Digital Communication , 1998 .

[7]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[8]  Imre Csiszár,et al.  Information Theory and Statistics: A Tutorial , 2004, Found. Trends Commun. Inf. Theory.

[9]  R. J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  Fu Jie Huang,et al.  A Tutorial on Energy-Based Learning , 2006 .

[12]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[13]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[14]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[16]  Bernhard Schölkopf,et al.  Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..

[17]  Blaine Nelson,et al.  Adversarial machine learning , 2019, AISec '11.

[18]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[19]  S. Shankar Sastry,et al.  Characterization and computation of local Nash equilibria in continuous games , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[20]  Pascal Vincent,et al.  Generalized Denoising Auto-Encoders as Generative Models , 2013, NIPS.

[21]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[22]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[23]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[24]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[25]  Yoshua Bengio,et al.  Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.

[26]  François Laviolette,et al.  Domain-Adversarial Neural Networks , 2014, ArXiv.

[27]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[28]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[29]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[30]  Zoubin Ghahramani,et al.  Training generative neural networks via Maximum Mean Discrepancy optimization , 2015, UAI.

[31]  Jon Gauthier.  Conditional generative adversarial nets for convolutional face generation , 2015 .

[32]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[33]  Ian J. Goodfellow,et al.  On distinguishability criteria for estimating generative models , 2014, ICLR.

[34]  Gabriel Kreiman,et al.  Unsupervised Learning of Visual Structure using Predictive Generative Networks , 2015, ArXiv.

[35]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[36]  Richard S. Zemel,et al.  Generative Moment Matching Networks , 2015, ICML.

[37]  Navdeep Jaitly,et al.  Adversarial Autoencoders , 2015, ArXiv.

[38]  Matt J. Kusner,et al.  GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution , 2016, ArXiv.

[39]  Chuan Li,et al.  Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.

[40]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.

[41]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[42]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Bogdan Raducanu,et al.  Invertible Conditional GANs for image editing , 2016, ArXiv.

[44]  David Pfau,et al.  Connecting Generative Adversarial Networks and Actor-Critic Methods , 2016, ArXiv.

[45]  Shakir Mohamed,et al.  Learning in Implicit Generative Models , 2016, ArXiv.

[46]  Yann LeCun,et al.  Energy-based Generative Adversarial Network , 2016, ICLR.

[47]  Hui Jiang,et al.  Generating images with recurrent adversarial networks , 2016, ArXiv.

[48]  Anil A. Bharath,et al.  Adversarial Training for Sketch Retrieval , 2016, ECCV Workshops.

[49]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[50]  Ming-Yu Liu,et al.  Coupled Generative Adversarial Networks , 2016, NIPS.

[51]  Matthias Bethge,et al.  A note on the evaluation of generative models , 2015, ICLR.

[52]  Martín Abadi,et al.  Learning to Protect Communications with Adversarial Neural Cryptography , 2016, ArXiv.

[53]  Masatoshi Uehara,et al.  Generative Adversarial Nets from a Density Ratio Estimation Perspective , 2016, 1610.02920.

[54]  Stefano Ermon,et al.  Generative Adversarial Imitation Learning , 2016, NIPS.

[55]  Razvan Pascanu,et al.  Progressive Neural Networks , 2016, ArXiv.

[56]  Jost Tobias Springenberg,et al.  Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks , 2015, ICLR.

[57]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[58]  Roland Vollgraf,et al.  Texture Synthesis with Spatial Generative Adversarial Networks , 2016, ArXiv.

[59]  Rob Fergus,et al.  Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks , 2016, ArXiv.

[60]  Bernt Schiele,et al.  Generative Adversarial Text to Image Synthesis , 2016, ICML.

[61]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

[62]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[63]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[64]  Amos J. Storkey,et al.  Censoring Representations with an Adversary , 2015, ICLR.

[65]  Abhinav Gupta,et al.  Generative Image Modeling Using Style and Structure Adversarial Networks , 2016, ECCV.

[66]  Aykut Erdem,et al.  Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts , 2016, ArXiv.

[67]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[68]  Sergey Levine,et al.  A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.

[69]  Kashino Kunio,et al.  Generative Adversarial Network-based Postfiltering for Statistical Parametric Speech Synthesis , 2016 .

[70]  Augustus Odena,et al.  Semi-Supervised Learning with Generative Adversarial Networks , 2016, ArXiv.

[71]  Xin Yu,et al.  Ultra-Resolving Face Images by Discriminative Generative Networks , 2016, ECCV.

[72]  Bernt Schiele,et al.  Learning What and Where to Draw , 2016, NIPS.

[73]  Eder Santana,et al.  Learning a Driving Simulator , 2016, ArXiv.

[74]  Yoshua Bengio,et al.  Professor Forcing: A New Algorithm for Training Recurrent Networks , 2016, NIPS.

[75]  Thomas Brox,et al.  Synthesizing the preferred inputs for neurons in neural networks via deep generator networks , 2016, NIPS.

[76]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[77]  Antonio Torralba,et al.  Generating Videos with Scene Dynamics , 2016, NIPS.

[78]  Olof Mogren,et al.  C-RNN-GAN: Continuous recurrent neural networks with adversarial training , 2016, ArXiv.

[79]  Zhe Gan,et al.  Generating Text via Adversarial Training , 2016 .

[80]  Alexei A. Efros,et al.  Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.

[81]  Sebastian Nowozin,et al.  f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.

[82]  Vishnu Naresh Boddeti,et al.  Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking , 2017, ArXiv.

[83]  Yoshua Bengio,et al.  Boundary-Seeking Generative Adversarial Networks , 2017, ICLR 2017.

[84]  Shunta Saito,et al.  Temporal Generative Adversarial Nets with Singular Value Clipping , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[85]  Mark Sandler,et al.  CycleGAN, a Master of Steganography , 2017, ArXiv.

[86]  Vaibhava Goel,et al.  McGan: Mean and Covariance Feature Matching GAN , 2017, ICML.

[87]  Trung Le,et al.  Multi-Generator Generative Adversarial Nets , 2017, ArXiv.

[88]  Yi Fang,et al.  Metric-based Generative Adversarial Network , 2017, ACM Multimedia.

[89]  Alán Aspuru-Guzik,et al.  Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.

[90]  Alan Ritter,et al.  Adversarial Learning for Neural Dialogue Generation , 2017, EMNLP.

[91]  Vighnesh Birodkar,et al.  Unsupervised Learning of Disentangled Representations from Video , 2017, NIPS.

[92]  Yoshua Bengio,et al.  Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[93]  Trevor Darrell,et al.  Adversarial Feature Learning , 2016, ICLR.

[94]  Andrew Brock,et al.  Neural Photo Editing with Introspective Adversarial Networks , 2016, ICLR.

[95]  Dumitru Erhan,et al.  Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[96]  Sepp Hochreiter,et al.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.

[97]  Sanja Fidler,et al.  Towards Diverse and Natural Image Descriptions via a Conditional GAN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[98]  Noel E. O'Connor,et al.  SalGAN: Visual Saliency Prediction with Generative Adversarial Networks , 2017, ArXiv.

[99]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[100]  Sebastian Nowozin,et al.  Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks , 2017, ICML.

[101]  Ben Poole,et al.  Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.

[102]  Ian J. Goodfellow,et al.  NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.

[103]  Yu Tsao,et al.  Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks , 2017, INTERSPEECH.

[104]  George Danezis,et al.  Generating steganographic images via adversarial training , 2017, NIPS.

[105]  Luc Van Gool,et al.  Pose Guided Person Image Generation , 2017, NIPS.

[106]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[107]  Casey S. Greene,et al.  Privacy-preserving generative deep neural networks support clinical data sharing , 2017 .

[108]  Jelmer M. Wolterink,et al.  Deep MR to CT Synthesis Using Unpaired Data , 2017, SASHIMI@MICCAI.

[109]  Dustin Tran,et al.  Hierarchical Implicit Models and Likelihood-Free Variational Inference , 2017, NIPS.

[110]  Jungwoo Lee,et al.  Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN , 2017, ArXiv.

[111]  Shakir Mohamed,et al.  Variational Approaches for Auto-Encoding Generative Adversarial Networks , 2017, ArXiv.

[112]  Jun Zhu,et al.  Triple Generative Adversarial Nets , 2017, NIPS.

[113]  Fan Yang,et al.  Good Semi-supervised Learning That Requires a Bad GAN , 2017, NIPS.

[114]  Lucas Theis,et al.  Amortised MAP Inference for Image Super-resolution , 2016, ICLR.

[115]  Martial Hebert,et al.  The Pose Knows: Video Forecasting by Generating Pose Futures , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[116]  Lei Zhang,et al.  CatGAN: Coupled Adversarial Transfer for Domain Generation , 2017, ArXiv.

[117]  Sridhar Mahadevan,et al.  Generative Multi-Adversarial Networks , 2016, ICLR.

[118]  Sebastian Nowozin,et al.  The Numerics of GANs , 2017, NIPS.

[119]  Trung Le,et al.  Dual Discriminator Generative Adversarial Nets , 2017, NIPS.

[120]  David Pfau,et al.  Unrolled Generative Adversarial Networks , 2016, ICLR.

[121]  Chi-Keung Tang,et al.  Conditional CycleGAN for Attribute Guided Face Image Generation , 2017, ArXiv.

[122]  Zhe Zhao,et al.  Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.

[123]  Yoshua Bengio,et al.  Maximum-Likelihood Augmented Discrete Generative Adversarial Networks , 2017, ArXiv.

[124]  Yunchao Wei,et al.  Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[125]  Yee Whye Teh,et al.  The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.

[126]  Jimeng Sun,et al.  Generating Multi-label Discrete Patient Records using Generative Adversarial Networks , 2017, MLHC.

[127]  Andrew Gordon Wilson,et al.  Bayesian GAN , 2017, NIPS.

[128]  Ran He,et al.  Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[129]  Jiwon Kim,et al.  Continual Learning with Deep Generative Replay , 2017, NIPS.

[130]  Yike Guo,et al.  Semantic Image Synthesis via Adversarial Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[131]  Sungroh Yoon,et al.  A SeqGAN for Polyphonic Music Generation , 2017, ArXiv.

[132]  Jae Hyun Lim,et al.  Geometric GAN , 2017, ArXiv.

[133]  Fei-Yue Wang,et al.  Generative adversarial networks: introduction and outlook , 2017, IEEE/CAA Journal of Automatica Sinica.

[134]  Zhe Gan,et al.  Triangle Generative Adversarial Networks , 2017, NIPS.

[135]  Yingyu Liang,et al.  Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.

[136]  Xi Chen,et al.  PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.

[137]  Tomas Pfister,et al.  Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[138]  Sebastian Nowozin,et al.  Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.

[139]  Concetto Spampinato,et al.  Semi Supervised Semantic Segmentation Using Generative Adversarial Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[140]  Sungroh Yoon,et al.  Domain Adaptation Using Adversarial Learning for Autonomous Navigation , 2017 .

[141]  Kun Xu,et al.  A survey of image synthesis and editing with generative adversarial networks , 2017 .

[142]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[143]  Dacheng Tao,et al.  Tag Disentangled Generative Adversarial Network for Object Image Re-rendering , 2017, IJCAI.

[144]  Yanjun Qi,et al.  Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently , 2017, ArXiv.

[145]  Hyunsoo Kim,et al.  Learning to Discover Cross-Domain Relations with Generative Adversarial Networks , 2017, ICML.

[146]  Charles A. Sutton,et al.  VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning , 2017, NIPS.

[147]  Min Sun,et al.  Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[148]  Xiangyang Xue,et al.  Semi-Latent GAN: Learning to generate and modify facial images from attributes , 2017, ArXiv.

[149]  Ying Tan,et al.  Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN , 2017, DMBD.

[150]  Jung-Woo Ha,et al.  Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning , 2017, ArXiv.

[151]  Alexander M. Rush,et al.  Adversarially Regularized Autoencoders for Generating Discrete Structures , 2017, ArXiv.

[152]  Minh N. Do,et al.  Semantic Image Inpainting with Deep Generative Models , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[153]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[154]  Hirokazu Kameoka,et al.  Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks , 2017, ArXiv.

[155]  Marcus Liwicki,et al.  TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network , 2017, ArXiv.

[156]  Bernhard Schölkopf,et al.  AdaGAN: Boosting Generative Models , 2017, NIPS.

[157]  Hirokazu Kameoka,et al.  Generative adversarial network-based postfilter for statistical parametric speech synthesis , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[158]  Mostapha Benhenda,et al.  ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? , 2017, ArXiv.

[159]  Léon Bottou,et al.  Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.

[160]  John E. Hopcroft,et al.  Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[161]  Otmar Hilliges,et al.  Guiding InfoGAN with Semi-supervision , 2017, ECML/PKDD.

[162]  Kevin Lin,et al.  Adversarial Ranking for Language Generation , 2017, NIPS.

[163]  David Berthelot,et al.  BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.

[164]  Ersin Yumer,et al.  Neural Face Editing with Intrinsic Image Disentangling , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[165]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[166]  Jing Dong,et al.  SSGAN: Secure Steganography Based on Generative Adversarial Networks , 2017, PCM.

[167]  Yiannis Demiris,et al.  MAGAN: Margin Adaptation for Generative Adversarial Networks , 2017, ArXiv.

[168]  Samy Bengio,et al.  Adversarial examples in the physical world , 2016, ICLR.

[169]  Xiaoming Liu,et al.  Disentangled Representation Learning GAN for Pose-Invariant Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[170]  Tao Mei,et al.  Deep Semantic Hashing with Generative Adversarial Networks , 2017, SIGIR.

[171]  Jan Kautz,et al.  Unsupervised Image-to-Image Translation Networks , 2017, NIPS.

[172]  Seunghoon Hong,et al.  Decomposing Motion and Content for Natural Video Sequence Prediction , 2017, ICLR.

[173]  Min Lin,et al.  Softmax GAN , 2017, ArXiv.

[174]  Abhinav Gupta,et al.  A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[175]  Jean-Luc Dugelay,et al.  Face aging with conditional generative adversarial networks , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[176]  Peter Dayan,et al.  Comparison of Maximum Likelihood and GAN-based training of Real NVPs , 2017, ArXiv.

[177]  Peng Zhang,et al.  IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models , 2017, SIGIR.

[178]  Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training , 2017, ICCV 2017.

[179]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[180]  Fei Xia,et al.  Understanding GANs: the LQG Setting , 2017, ArXiv.

[181]  Dhruv Batra,et al.  LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation , 2016, ICLR.

[182]  Yiming Yang,et al.  MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.

[183]  Yi Zhang,et al.  Theoretical limitations of Encoder-Decoder GAN architectures , 2017, ArXiv.

[184]  Marc G. Bellemare,et al.  The Cramer Distance as a Solution to Biased Wasserstein Gradients , 2017, ArXiv.

[185]  Gunnar Rätsch,et al.  Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs , 2017, ArXiv.

[186]  Ravi Kiran Sarvadevabhatla,et al.  DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[187]  Hiroshi Ishikawa,et al.  Globally and locally consistent image completion , 2017, ACM Trans. Graph..

[188]  Roland Vollgraf,et al.  Learning Texture Manifolds with the Periodic Spatial GAN , 2017, ICML.

[189]  Alexei A. Efros,et al.  Toward Multimodal Image-to-Image Translation , 2017, NIPS.

[190]  Alexander J. Smola,et al.  Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy , 2016, ICLR.

[191]  Raja Bala,et al.  Semi-supervised Conditional GANs , 2017, ArXiv.

[192]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[193]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[194]  Tom Sercu,et al.  Fisher GAN , 2017, NIPS.

[195]  Dustin Tran,et al.  Deep and Hierarchical Implicit Models , 2017, ArXiv.

[196]  James Davidson,et al.  Supervision via competition: Robot adversaries for learning tasks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[197]  Gang Hua,et al.  CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[198]  José Bento,et al.  Generative Adversarial Active Learning , 2017, ArXiv.

[199]  Dimitris N. Metaxas,et al.  StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[200]  Ambedkar Dukkipati,et al.  Image Generation and Editing with Variational Info Generative AdversarialNetworks , 2017, ArXiv.

[201]  Eric P. Xing,et al.  Dual Motion GAN for Future-Flow Embedded Video Prediction , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[202]  Chunhua Shen,et al.  Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition , 2017 .

[203]  Byoungjip Kim,et al.  Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks , 2017, ArXiv.

[204]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[205]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[206]  Yuan Li,et al.  SCAN: Structure Correcting Adversarial Network for Chest X-rays Organ Segmentation , 2017, ArXiv.

[207]  Ping Tan,et al.  DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[208]  Yoshua Bengio,et al.  Mode Regularized Generative Adversarial Networks , 2016, ICLR.

[209]  Brendan J. Frey,et al.  Generating and designing DNA with deep generative models , 2017, ArXiv.

[210]  Yi Yang,et al.  GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data , 2017, BMVC 2017.

[211]  Antonio Bonafonte,et al.  SEGAN: Speech Enhancement Generative Adversarial Network , 2017, INTERSPEECH.

[212]  Fang Zhao,et al.  Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis , 2017, NIPS.

[213]  Qi Zhao,et al.  Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[214]  Yi Yang,et al.  Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[215]  Aaron C. Courville,et al.  Adversarially Learned Inference , 2016, ICLR.

[216]  Lior Wolf,et al.  One-Sided Unsupervised Domain Mapping , 2017, NIPS.

[217]  Lawrence Carin,et al.  ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching , 2017, NIPS.

[218]  Christopher Krügel,et al.  Detecting Deceptive Reviews Using Generative Adversarial Networks , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[219]  James Zou,et al.  Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions , 2018, ArXiv.

[220]  Hayit Greenspan,et al.  GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.

[221]  Xin Wang,et al.  No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling , 2018, ACL.

[222]  Radu Timofte,et al.  2018 PIRM Challenge on Perceptual Image Super-resolution , 2018, ArXiv.

[223]  Tatsuya Harada,et al.  Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[224]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[225]  Chi-Keung Tang,et al.  Attribute-Guided Face Generation Using Conditional CycleGAN , 2017, ECCV.

[226]  Siyuan Liu,et al.  Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[227]  Jonas Adler,et al.  Banach Wasserstein GAN , 2018, NeurIPS.

[228]  Adam Finkelstein,et al.  PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[229]  Xi Peng,et al.  A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[230]  Ming-Hsuan Yang,et al.  Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[231]  Yu Qiao,et al.  ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.

[232]  Eric P. Xing,et al.  SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays , 2017, DLMIA/ML-CDS@MICCAI.

[233]  Yu Cheng,et al.  Sobolev GAN , 2017, ICLR.

[234]  Andreas Krause,et al.  An Online Learning Approach to Generative Adversarial Networks , 2017, ICLR.

[235]  Tonio Ball,et al.  EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals , 2018, ArXiv.

[236]  Tao Xu,et al.  SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation , 2017, Neuroinformatics.

[237]  Jung-Woo Ha,et al.  StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[238]  Alexandros G. Dimakis,et al.  CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training , 2017, ICLR.

[239]  Yung-Yu Chuang,et al.  Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[240]  David Tse,et al.  A Convex Duality Framework for GANs , 2018, NeurIPS.

[241]  Jacob Abernethy,et al.  On Convergence and Stability of GANs , 2018 .

[242]  Chris Donahue,et al.  Synthesizing Audio with Generative Adversarial Networks , 2018, ArXiv.

[243]  Hao Zhu,et al.  High-Resolution Talking Face Generation via Mutual Information Approximation , 2018, ArXiv.

[244]  Maneesh Kumar Singh,et al.  DRIT++: Diverse Image-to-Image Translation via Disentangled Representations , 2019, International Journal of Computer Vision.

[245]  Pieter Abbeel,et al.  Learning Plannable Representations with Causal InfoGAN , 2018, NeurIPS.

[246]  Shinnosuke Takamichi,et al.  Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[247]  Silvio Savarese,et al.  Adversarial Feature Augmentation for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[248]  Ali Farhadi,et al.  SeGAN: Segmenting and Generating the Invisible , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[249]  Won-Ki Jeong,et al.  Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.

[250]  Andrew M. Dai,et al.  Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step , 2017, ICLR.

[251]  Silvio Savarese,et al.  Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[252]  Tao Xu,et al.  On the Discrimination-Generalization Tradeoff in GANs , 2017, ICLR.

[253]  William Yang Wang,et al.  DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction , 2018, ACL.

[254]  Oriol Vinyals,et al.  Synthesizing Programs for Images using Reinforced Adversarial Learning , 2018, ICML.

[255]  Guoyin Wang,et al.  Generative Adversarial Network Training is a Continual Learning Problem , 2018, ArXiv.

[256]  Ke Wang,et al.  SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks , 2018, IJCAI.

[257]  Saifuddin Hitawala,et al.  Comparative Study on Generative Adversarial Networks , 2018, ArXiv.

[258]  Shiguang Shan,et al.  Duplex Generative Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[259]  Shunyu Yao,et al.  3D-Aware Scene Manipulation via Inverse Graphics , 2018, NeurIPS.

[260]  Fabio Tozeto Ramos,et al.  Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference , 2018, ArXiv.

[261]  Jian Cheng,et al.  Semi-supervised Generative Adversarial Hashing for Image Retrieval , 2018, ECCV.

[262]  Jan Kautz,et al.  Multimodal Unsupervised Image-to-Image Translation , 2018, ECCV.

[263]  Asja Fischer,et al.  On the regularization of Wasserstein GANs , 2017, ICLR.

[264]  Yong-Jin Liu,et al.  CartoonGAN: Generative Adversarial Networks for Photo Cartoonization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[265]  Constantinos Daskalakis,et al.  Training GANs with Optimism , 2017, ICLR.

[266]  Ashish Khetan,et al.  Robustness of Conditional GANs to Noisy Labels , 2018, NeurIPS.

[267]  David A. Wagner,et al.  Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.

[268]  Yuxin Peng,et al.  Unsupervised Generative Adversarial Cross-modal Hashing , 2017, AAAI.

[269]  Leon Sixt,et al.  RenderGAN: Generating Realistic Labeled Data , 2016, Front. Robot. AI.

[270]  Thomas S. Huang,et al.  Generative Image Inpainting with Contextual Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[271]  Sebastian Nowozin,et al.  Which Training Methods for GANs do actually Converge? , 2018, ICML.

[272]  Alexei A. Efros,et al.  Learning Beyond Human Expertise with Generative Models for Dental Restorations , 2018, ArXiv.

[273]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[274]  Lu Zhang,et al.  FairGAN: Fairness-aware Generative Adversarial Networks , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[275]  Sergey Levine,et al.  Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[276]  William Yang Wang,et al.  KBGAN: Adversarial Learning for Knowledge Graph Embeddings , 2017, NAACL.

[277]  Dacheng Tao,et al.  Perceptual Adversarial Networks for Image-to-Image Transformation , 2017, IEEE Transactions on Image Processing.

[278]  Zhenan Sun,et al.  Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization , 2018, NeurIPS.

[279]  Cheng Deng,et al.  Unsupervised Deep Generative Adversarial Hashing Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[280]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[281]  Nicu Sebe,et al.  Deformable GANs for Pose-Based Human Image Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[282]  Dong Liu,et al.  Fully Convolutional Adaptation Networks for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[283]  Rynson W. H. Lau,et al.  VITAL: VIsual Tracking via Adversarial Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[284]  Andrew M. Dai,et al.  MaskGAN: Better Text Generation via Filling in the ______ , 2018, ICLR.

[285]  Francesc Moreno-Noguer,et al.  GANimation: Anatomically-aware Facial Animation from a Single Image , 2018, ECCV.

[286]  Philip H. S. Torr,et al.  Multi-agent Diverse Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[287]  Yuichi Yoshida,et al.  Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.

[288]  Yaser Sheikh,et al.  Recycle-GAN: Unsupervised Video Retargeting , 2018, ECCV.

[289]  Zhe Gan,et al.  AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[290]  Jiebo Luo,et al.  Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training , 2018, AAAI.

[291]  Jun Zhu,et al.  Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[292]  Dawn Xiaodong Song,et al.  Adversarial Examples for Generative Models , 2017, 2018 IEEE Security and Privacy Workshops (SPW).

[293]  Taesung Park,et al.  CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.

[294]  Michael I. Jordan,et al.  Conditional Adversarial Domain Adaptation , 2017, NeurIPS.

[295]  Arthur Gretton,et al.  Demystifying MMD GANs , 2018, ICLR.

[296]  Masatoshi Yoshikawa,et al.  Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training , 2018, ACM Multimedia.

[297]  Qiong Zhang,et al.  Generating Handwritten Chinese Characters Using CycleGAN , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[298]  Ming Yang,et al.  Conditional Generative Adversarial Network for Structured Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[299]  Philip Bachman,et al.  Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data , 2018, ICML.

[300]  Chao Dong,et al.  Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[301]  M. Zaheer,et al.  Nonparametric Density Estimation under Adversarial Losses , 2018, NeurIPS.

[302]  Eric P. Xing,et al.  Generative Semantic Manipulation with Mask-Contrasting GAN , 2018, ECCV.

[303]  Kilian Q. Weinberger,et al.  An empirical study on evaluation metrics of generative adversarial networks , 2018, ArXiv.

[304]  Bin Gao,et al.  Rare Query Expansion Through Generative Adversarial Networks in Search Advertising , 2018, KDD.

[305]  Takeru Miyato,et al.  cGANs with Projection Discriminator , 2018, ICLR.

[306]  Stefano Ermon,et al.  Multi-Agent Generative Adversarial Imitation Learning , 2018, NeurIPS.

[307]  Jason D. Lee,et al.  On the Convergence and Robustness of Training GANs with Regularized Optimal Transport , 2018, NeurIPS.

[308]  Yu Cheng,et al.  Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing , 2018, ACM Multimedia.

[309]  Jian Shen,et al.  Wasserstein Distance Guided Representation Learning for Domain Adaptation , 2017, AAAI.

[310]  Ajmal Mian,et al.  Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.

[311]  Lukasz Kaiser,et al.  Unsupervised Cipher Cracking Using Discrete GANs , 2018, ICLR.

[312]  Jie Zhang,et al.  Semi-supervised Learning on Graphs with Generative Adversarial Nets , 2018, CIKM.

[313]  Satinder Singh,et al.  Generative Adversarial Self-Imitation Learning , 2018, ArXiv.

[314]  Andrea Vedaldi,et al.  It Takes (Only) Two: Adversarial Generator-Encoder Networks , 2017, AAAI.

[315]  Alexandros G. Dimakis,et al.  AmbientGAN: Generative models from lossy measurements , 2018, ICLR.

[316]  Jingkuan Song,et al.  Binary Generative Adversarial Networks for Image Retrieval , 2017, AAAI.

[317]  Luc Van Gool,et al.  Wasserstein Divergence for GANs , 2017, ECCV.

[318]  Tengyuan Liang,et al.  On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results , 2018, ArXiv.

[319]  Yi Zhang,et al.  Do GANs learn the distribution? Some Theory and Empirics , 2018, ICLR.

[320]  Jascha Sohl-Dickstein,et al.  Adversarial Examples that Fool both Computer Vision and Time-Limited Humans , 2018, NeurIPS.

[321]  Eric P. Xing,et al.  On Unifying Deep Generative Models , 2017, ICLR.

[322]  Chris Donahue,et al.  Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[323]  Xiaohua Zhai,et al.  Self-Supervised Generative Adversarial Networks , 2018, ArXiv.

[324]  Jakub W. Pachocki,et al.  Emergent Complexity via Multi-Agent Competition , 2017, ICLR.

[325]  Xiangyu Liu,et al.  Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[326]  Cristian Canton-Ferrer,et al.  Eye In-painting with Exemplar Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[327]  Aljoscha Smolic,et al.  AlphaGAN: Generative adversarial networks for natural image matting , 2018, BMVC.

[328]  Jan Kautz,et al.  Video-to-Video Synthesis , 2018, NeurIPS.

[329]  Ruben Villegas,et al.  Neural Kinematic Networks for Unsupervised Motion Retargetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[330]  Yongqiang Zhang,et al.  SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network , 2018, ECCV.

[331]  Toniann Pitassi,et al.  Learning Adversarially Fair and Transferable Representations , 2018, ICML.

[332]  Blake Lemoine,et al.  Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.

[333]  Tanveer F. Syeda-Mahmood,et al.  Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[334]  Mario Lucic,et al.  Are GANs Created Equal? A Large-Scale Study , 2017, NeurIPS.

[335]  He Ma,et al.  Quantitatively Evaluating GANs With Divergences Proposed for Training , 2018, ICLR.

[336]  Junichi Yamagishi,et al.  High-Quality Nonparallel Voice Conversion Based on Cycle-Consistent Adversarial Network , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[337]  Seunghoon Hong,et al.  Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[338]  Chris Donahue,et al.  Semantically Decomposing the Latent Spaces of Generative Adversarial Networks , 2017, ICLR.

[339]  Dacheng Tao,et al.  Attention-GAN for Object Transfiguration in Wild Images , 2018, ECCV.

[340]  Alexander M. Rush,et al.  Adversarially Regularized Autoencoders , 2017, ICML.

[341]  Hazim Kemal Ekenel,et al.  Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[342]  José M. F. Moura,et al.  Adversarial Multiple Source Domain Adaptation , 2018, NeurIPS.

[343]  Swami Sankaranarayanan,et al.  Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[344]  Jan Kautz,et al.  MoCoGAN: Decomposing Motion and Content for Video Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[345]  Zheng Xu,et al.  Stabilizing Adversarial Nets With Prediction Methods , 2017, ICLR.

[346]  Iasonas Kokkinos,et al.  Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance , 2018, ECCV.

[347]  Jan Kautz,et al.  High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[348]  Atul Prakash,et al.  Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[349]  Luc Van Gool,et al.  ComboGAN: Unrestrained Scalability for Image Domain Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[350]  Rama Chellappa,et al.  Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.

[351]  Ghislain St-Yves,et al.  Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images , 2018, bioRxiv.

[352]  Stefanos Zafeiriou,et al.  Robust Conditional Generative Adversarial Networks , 2018, ICLR.

[353]  Luuk J. Spreeuwers,et al.  A Layer-Based Sequential Framework for Scene Generation with GANs , 2019, AAAI.

[354]  Yi Li,et al.  Biphasic Learning of GANs for High-Resolution Image-to-Image Translation , 2019, ArXiv.

[355]  Bo Jiang,et al.  MisGAN: Learning from Incomplete Data with Generative Adversarial Networks , 2019, ICLR.

[356]  Xiaoming Liu,et al.  Representation Learning by Rotating Your Faces , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[357]  Yanjie Fu,et al.  Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning , 2019, KDD.

[358]  Chris Donahue,et al.  Adversarial Audio Synthesis , 2018, ICLR.

[359]  Yuhui Zheng,et al.  Recent Progress on Generative Adversarial Networks (GANs): A Survey , 2019, IEEE Access.

[360]  Kaiqi Huang,et al.  GP-GAN: Towards Realistic High-Resolution Image Blending , 2017, ACM Multimedia.

[361]  Hui Xiong,et al.  Adversarial Substructured Representation Learning for Mobile User Profiling , 2019, KDD.

[362]  Jeff Donahue,et al.  Adversarial Video Generation on Complex Datasets , 2019 .

[363]  Yuan Qi,et al.  Generative Adversarial User Model for Reinforcement Learning Based Recommendation System , 2018, ICML.

[364]  Xintao Wu,et al.  Achieving Causal Fairness through Generative Adversarial Networks , 2019, IJCAI.

[365]  Guido Imbens,et al.  Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations , 2019, Journal of Econometrics.

[366]  Yoshua Bengio,et al.  Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[367]  Yu Qiao,et al.  RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[368]  Chao Li,et al.  DRGAN: A GAN-Based Framework for Doctor Recommendation in Chinese On-Line QA Communities , 2019, DASFAA.

[369]  Sungroh Yoon,et al.  How Generative Adversarial Networks and Their Variants Work , 2017, ACM Comput. Surv..

[370]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[371]  Sheng-Jun Huang,et al.  Learning Class-Conditional GANs with Active Sampling , 2019, KDD.

[372]  Jeff Donahue,et al.  Efficient Video Generation on Complex Datasets , 2019, ArXiv.

[373]  Lei Shi,et al.  MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks , 2019, ICANN.

[374]  Tomas E. Ward,et al.  Generative Adversarial Networks in Computer Vision , 2019, ACM Comput. Surv..

[375]  Smita Krishnaswamy,et al.  TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[376]  Nicu Sebe,et al.  Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[377]  Jieping Ye,et al.  Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation , 2019, KDD.

[378]  Jiashi Feng,et al.  Anticipating Where People will Look Using Adversarial Networks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[379]  Fang Liu,et al.  Task-Oriented GAN for PolSAR Image Classification and Clustering , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[380]  Shakir Mohamed,et al.  Training language GANs from Scratch , 2019, NeurIPS.

[381]  Xianwen Yu,et al.  VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders , 2019, IJCAI.

[382]  Xiaochun Cao,et al.  ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[383]  J. Schmidhuber.  Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization , 2019, ArXiv.

[384]  Ji-Rong Wen,et al.  PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data , 2019, SIGIR.

[385]  Tomas E. Ward,et al.  Generative Adversarial Networks: A Survey and Taxonomy , 2019, ArXiv.

[386]  Tieniu Tan,et al.  Wavelet Domain Generative Adversarial Network for Multi-scale Face Hallucination , 2019, International Journal of Computer Vision.

[387]  Xiaohua Zhai,et al.  A Large-Scale Study on Regularization and Normalization in GANs , 2018, ICML.

[388]  Qiaozhu Mei,et al.  Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation , 2019, EMNLP.

[389]  Xiaogang Wang,et al.  StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[390]  Shin Ishii,et al.  Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[391]  Guo-Jun Qi,et al.  Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities , 2017, International Journal of Computer Vision.

[392]  Xiao Liu,et al.  STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[393]  Tie-Yan Liu,et al.  Multi-Agent Dual Learning , 2019, ICLR.

[394]  Wenjing Wang,et al.  TET-GAN: Text Effects Transfer via Stylization and Destylization , 2019, AAAI.

[395]  Shahram Latifi,et al.  Audio Enhancement and Synthesis using Generative Adversarial Networks: A Survey , 2019, International Journal of Computer Applications.

[396]  Anil A. Bharath,et al.  Inverting the Generator of a Generative Adversarial Network , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[397]  Jing Zhang,et al.  MirrorGAN: Learning Text-To-Image Generation by Redescription , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[398]  A. Uppal,et al.  Nonparametric density estimation & convergence of GANs under Besov IPM losses , 2019 .

[399]  Xu Jia,et al.  Co-Evolutionary Compression for Unpaired Image Translation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[400]  Stefanos Zafeiriou,et al.  GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[401]  Wei Liu,et al.  TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation , 2019, ArXiv.

[402]  Shuicheng Yan,et al.  PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer , 2019, ArXiv.

[403]  Zhenan Sun,et al.  3D Aided Duet GANs for Multi-View Face Image Synthesis , 2019, IEEE Transactions on Information Forensics and Security.

[404]  Gauthier Gidel,et al.  A Variational Inequality Perspective on Generative Adversarial Networks , 2018, ICLR.

[405]  Jian Pei,et al.  ProGAN: Network Embedding via Proximity Generative Adversarial Network , 2019, KDD.

[406]  Morteza Mardani,et al.  Deep Generative Adversarial Neural Networks for Compressive Sensing MRI , 2019, IEEE Transactions on Medical Imaging.

[407]  Chuan Shi,et al.  Adversarial Learning on Heterogeneous Information Networks , 2019, KDD.

[408]  Quoc Viet Hung Nguyen,et al.  Enhancing Collaborative Filtering with Generative Augmentation , 2019, KDD.

[409]  Ran He,et al.  Dual Variational Generation for Low-Shot Heterogeneous Face Recognition , 2019, NeurIPS.

[410]  Fuxin Li,et al.  HyperGAN: A Generative Model for Diverse, Performant Neural Networks , 2019, ICML.

[411]  Mihaela van der Schaar,et al.  KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks , 2018, ICLR.

[412]  Yan Wu,et al.  LOGAN: Latent Optimisation for Generative Adversarial Networks , 2019, ArXiv.

[413]  Amaia,et al.  Wav2Pix: Speech-conditioned Face Generation Using Generative Adversarial Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[414]  Ran Yi,et al.  APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[415]  Vaishnavh Nagarajan.  Theoretical Insights into Memorization in GANs , 2019 .

[416]  Sergey Levine,et al.  Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow , 2018, ICLR.

[417]  A. Smeaton,et al.  Neuroscore: A Brain-inspired Evaluation Metric for Generative Adversarial Networks , 2019, ArXiv.

[418]  Martin Wattenberg,et al.  GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation , 2018, IEEE Transactions on Visualization and Computer Graphics.

[419]  Yong Luo,et al.  ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning , 2019, CIKM.

[420]  Cheng Pan,et al.  SRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks , 2019, ArXiv.

[421]  Michal Irani,et al.  InGAN: Capturing and Retargeting the “DNA” of a Natural Image , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[422]  Hal Daumé,et al.  Answer-based Adversarial Training for Generating Clarification Questions , 2019, NAACL.

[423]  Carlos R. Ponce,et al.  Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences , 2019, Cell.

[424]  Taesung Park,et al.  Semantic Image Synthesis With Spatially-Adaptive Normalization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[425]  Bolei Zhou,et al.  Seeing What a GAN Cannot Generate , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[426]  Zhen Wang,et al.  On the Effectiveness of Least Squares Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[427]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[428]  Madian Khabsa,et al.  Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching , 2018, AAAI.

[429]  Shiguang Shan,et al.  AttGAN: Facial Attribute Editing by Only Changing What You Want , 2017, IEEE Transactions on Image Processing.

[430]  Roland Vollgraf,et al.  Generating High-Resolution Fashion Model Images Wearing Custom Outfits , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[431]  Jingrui He,et al.  Task-Adversarial Co-Generative Nets , 2019, KDD.

[432]  Yongdong Zhang,et al.  APE-GAN: Adversarial Perturbation Elimination with GAN , 2017, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[433]  Sergey Levine,et al.  Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[434]  Yu Bai,et al.  Approximability of Discriminators Implies Diversity in GANs , 2018, ICLR.

[435]  Xin Yao,et al.  Evolutionary Generative Adversarial Networks , 2018, IEEE Transactions on Evolutionary Computation.

[436]  Tali Dekel,et al.  SinGAN: Learning a Generative Model From a Single Natural Image , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[437]  Jeff Donahue,et al.  Large Scale Adversarial Representation Learning , 2019, NeurIPS.

[438]  Timo Aila,et al.  A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[439]  Siwei Ma,et al.  Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[440]  Jordi Torres,et al.  Wav2Pix: Speech-conditioned Face Generation Using Generative Adversarial Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[441]  Ali Borji,et al.  Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..

[442]  Alexia Jolicoeur-Martineau,et al.  The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.

[443]  Liujuan Cao,et al.  Towards Optimal Structured CNN Pruning via Generative Adversarial Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[444]  Bolei Zhou,et al.  GAN Dissection: Visualizing and Understanding Generative Adversarial Networks , 2018, ICLR.

[445]  Ioannis Mitliagkas,et al.  Multi-objective training of Generative Adversarial Networks with multiple discriminators , 2019, ICML.

[446]  Maciej Zieba,et al.  Generative Adversarial Networks: recent developments , 2019, ICAISC.

[447]  Ling Shao,et al.  Generative Reconstructive Hashing for Incomplete Video Analysis , 2019, ACM Multimedia.

[448]  Alexei A. Efros,et al.  Everybody Dance Now , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[449]  Stephan Günnemann,et al.  Adversarial Attacks on Neural Networks for Graph Data , 2018, KDD.

[450]  Xiaohua Zhai,et al.  High-Fidelity Image Generation With Fewer Labels , 2019, ICML.

[451]  Jaakko Lehtinen,et al.  Few-Shot Unsupervised Image-to-Image Translation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[452]  Zhenan Sun,et al.  Attribute-Aware Face Aging With Wavelet-Based Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[453]  Shuicheng Yan,et al.  3D-Aided Dual-Agent GANs for Unconstrained Face Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[454]  Alan F. Smeaton,et al.  Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation , 2018, Cognitive Computation.

[455]  David Tse,et al.  Deconstructing Generative Adversarial Networks , 2019, IEEE Transactions on Information Theory.

[456]  Tero Karras,et al.  Analyzing and Improving the Image Quality of StyleGAN , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[457]  Evgeny Burnaev,et al.  Steganographic generative adversarial networks , 2017, International Conference on Machine Vision.

[458]  Alan F. Smeaton,et al.  Synthetic-Neuroscore: Using a neuro-AI interface for evaluating generative adversarial networks , 2019, Neurocomputing.

[459]  J. Schmidhuber.  Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991) , 2019, Neural Networks.

[460]  Wei Liu,et al.  End-to-End Single Image Fog Removal Using Enhanced Cycle Consistent Adversarial Networks , 2019, IEEE Transactions on Image Processing.

[461]  Shuicheng Yan,et al.  PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[462]  Phillip Isola,et al.  On the "steerability" of generative adversarial networks , 2019, ICLR.

[463]  Ashish Khetan,et al.  PacGAN: The Power of Two Samples in Generative Adversarial Networks , 2017, IEEE Journal on Selected Areas in Information Theory.

[464]  Yuxin Peng,et al.  SCH-GAN: Semi-Supervised Cross-Modal Hashing by Generative Adversarial Network , 2018, IEEE Transactions on Cybernetics.

[465]  Feiping Nie,et al.  WeGAN: Deep Image Hashing With Weighted Generative Adversarial Networks , 2020, IEEE Transactions on Multimedia.

[466]  Chunyun Zhang,et al.  Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs , 2020, AAAI.

[467]  Shiv Ram Dubey,et al.  CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation , 2019, Expert Syst. Appl..