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Awesome papers about Generative Adversarial Networks. Majority of papers are related to Image Translation.
Please help contribute this list by contacting [Me][zhang163220@gmail.com] or add pull request
[Generative Adversarial Nets]
[UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION]
[Image-to-image translation using conditional adversarial nets]
[Learning to Discover Cross-Domain Relations with Generative Adversarial Networks]
[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks]
[CoGAN: Coupled Generative Adversarial Networks]
[Unsupervised Image-to-Image Translation with Generative Adversarial Networks]
[DualGAN: Unsupervised Dual Learning for Image-to-Image Translation]
[Unsupervised Image-to-Image Translation Networks]
[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs]
[XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings]
[UNIT: UNsupervised Image-to-image Translation Networks]
[Toward Multimodal Image-to-Image Translation]
[Multimodal Unsupervised Image-to-Image Translation]
[Video-to-Video Synthesis]
[Everybody Dance Now]
[Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation]
[Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation]
[Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation]
[StarGAN v2: Diverse Image Synthesis for Multiple Domains]
[Structural-analogy from a Single Image Pair]
[High-Resolution Daytime Translation Without Domain Labels]
[Rethinking the Truly Unsupervised Image-to-Image Translation]
[Diverse Image Generation via Self-Conditioned GANs]
[Contrastive Learning for Unpaired Image-to-Image Translation]
[Autoencoding beyond pixels using a learned similarity metric]
[Coupled Generative Adversarial Networks]
[Invertible Conditional GANs for image editing]
[Learning Residual Images for Face Attribute Manipulation]
[Neural Photo Editing with Introspective Adversarial Networks]
[Neural Face Editing with Intrinsic Image Disentangling]
[GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ]
[Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis]
[StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation]
[Arbitrary Facial Attribute Editing: Only Change What You Want]
[ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes]
[Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation]
[GANimation: Anatomically-aware Facial Animation from a Single Image]
[Geometry Guided Adversarial Facial Expression Synthesis]
[STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing]
[3d guided fine-grained face manipulation] [Paper](CVPR 2019)
[SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color]
[A Survey of Deep Facial Attribute Analysis]
[PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing]
[SSCGAN: Facial Attribute Editing via StyleSkip Connections]
[CAFE-GAN: Arbitrary Face Attribute Editingwith Complementary Attention Feature]
[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]
[Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks]
[Generative Adversarial Text to Image Synthesis]
[Improved Techniques for Training GANs]
[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space]
[StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks]
[Improved Training of Wasserstein GANs]
[Boundary Equibilibrium Generative Adversarial Networks]
[Progressive Growing of GANs for Improved Quality, Stability, and Variation]
[ Self-Attention Generative Adversarial Networks ]
[Large Scale GAN Training for High Fidelity Natural Image Synthesis]
[A Style-Based Generator Architecture for Generative Adversarial Networks]
[Analyzing and Improving the Image Quality of StyleGAN]
[SinGAN: Learning a Generative Model from a Single Natural Image]
[Real or Not Real, that is the Question]
[Training End-to-end Single Image Generators without GANs]
[Adversarial Latent Autoencoders]
[DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation]
[Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks]
[GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks]
[MGGR: MultiModal-Guided Gaze Redirection with Coarse-to-Fine Learning]
[Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild]
[AutoGAN: Neural Architecture Search for Generative Adversarial Networks]
[Animating arbitrary objects via deep motion transfer]
[First Order Motion Model for Image Animation]
[Energy-based generative adversarial network]
[Improved Techniques for Training GANs]
[Mode Regularized Generative Adversarial Networks]
[Improving Generative Adversarial Networks with Denoising Feature Matching]
[Sampling Generative Networks]
[How to train Gans]
[Towards Principled Methods for Training Generative Adversarial Networks]
[Unrolled Generative Adversarial Networks]
[Least Squares Generative Adversarial Networks]
[Wasserstein GAN]
[Improved Training of Wasserstein GANs]
[Towards Principled Methods for Training Generative Adversarial Networks]
[Generalization and Equilibrium in Generative Adversarial Nets]
[GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium]
[Spectral Normalization for Generative Adversarial Networks]
[Which Training Methods for GANs do actually Converge]
[Self-Supervised Generative Adversarial Networks]
[Semantic Image Inpainting with Perceptual and Contextual Losses]
[Context Encoders: Feature Learning by Inpainting]
[Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks]
[Generative face completion]
[Globally and Locally Consistent Image Completion]
[High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis]
[Eye In-Painting with Exemplar Generative Adversarial Networks]
[Generative Image Inpainting with Contextual Attention]
[Free-Form Image Inpainting with Gated Convolution]
[EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning]
[a layer-based sequential framework for scene generation with gans]
[Adversarial Training Methods for Semi-Supervised Text Classification]
[Improved Techniques for Training GANs]
[Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks]
[Semi-Supervised QA with Generative Domain-Adaptive Nets]
[Good Semi-supervised Learning that Requires a Bad GAN]
[AdaGAN: Boosting Generative Models]
[GP-GAN: Towards Realistic High-Resolution Image Blending]
[Joint Discriminative and Generative Learning for Person Re-identification]
[Pose-Normalized Image Generation for Person Re-identification]
[Image super-resolution through deep learning]
[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]
[EnhanceGAN]
[ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks]
[Robust LSTM-Autoencoders for Face De-Occlusion in the Wild]
[Adversarial Deep Structural Networks for Mammographic Mass Segmentation]
[Semantic Segmentation using Adversarial Networks]
[Perceptual generative adversarial networks for small object detection]
[A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection]
[Style aggregated network for facial landmark detection]
[Conditional Generative Adversarial Nets]
[InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets]
[Conditional Image Synthesis With Auxiliary Classifier GANs]
[Pixel-Level Domain Transfer]
[Invertible Conditional GANs for image editing]
[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space]
[StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks]
[Deep multi-scale video prediction beyond mean square error]
[Generating Videos with Scene Dynamics]
[MoCoGAN: Decomposing Motion and Content for Video Generation]
[ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal]
[BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network]
[Connecting Generative Adversarial Networks and Actor-Critic Methods]
[C-RNN-GAN: Continuous recurrent neural networks with adversarial training]
[SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient]
[Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery]
[Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling]
[Transformation-Grounded Image Generation Network for Novel 3D View Synthesis]
[MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions]
[Maximum-Likelihood Augmented Discrete Generative Adversarial Networks]
[Boundary-Seeking Generative Adversarial Networks]
[GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution]
[Generative OpenMax for Multi-Class Open Set Classification]
[Controllable Invariance through Adversarial Feature Learning]
[Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro]
[Learning from Simulated and Unsupervised Images through Adversarial Training]
[GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification]
[cleverhans]
[reset-cppn-gan-tensorflow]
[HyperGAN]
| Author | Address |
|---|---|
| inFERENCe | Adversarial network |
| inFERENCe | InfoGan |
| distill | Deconvolution and Image Generation |
| yingzhenli | Gan theory |
| OpenAI | Generative model |
[1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
[2] [PDF](NIPS Lecun Slides)
[3] [ICCV 2017 Tutorial About GANS]
[3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)]
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