[
  {
    "path": "README.md",
    "content": "# gans_paper_collection\n\n## Review or Survey\n### NIPS 2016 Tutorial Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1701.00160)\n### Generative Adversarial Networks An Overview [[paper]](https://arxiv.org/abs/1710.07035)\n\n## 2023\n### Scaling up GANs for Text-to-Image Synthesis CVPR 2023 [[arXiv paper]](https://arxiv.org/abs/2303.05511) [[project]](https://mingukkang.github.io/GigaGAN/)\n\n## 2014\n### Generative Adversarial Nets [[paper]](https://papers.nips.cc/paper/5423-generative-adversarial-nets) [[code]](https://github.com/goodfeli/adversarial)\n### Conditional Generative Adversarial Nets [[paper]](https://arxiv.org/abs/1411.1784)\n\n## 2015\n### Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks [[paper]](https://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks)\n### Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1511.06434) [[code]](https://github.com/Newmu/dcgan_code)\n### Adversarial Autoencoders [[paper]](https://arxiv.org/abs/1511.05644)\n### Deep Multi-scale Video Prediction Beyond Mean Square Error [[paper]](https://arxiv.org/abs/1511.05440)\n### Autoencoding beyond Pixels Using a Learned Similarity Metric [[paper]](https://arxiv.org/abs/1512.09300)\n### A Note on the Evaluation of Generative Models [[paper]](https://arxiv.org/abs/1511.01844)\n\n## 2016\n### (pix2pix) Image-to-image Translation with Conditional Adversarial Networks [[paper]](https://arxiv.org/abs/1611.07004) [[Pytorch code]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Torch code]](https://github.com/phillipi/pix2pix) [[TF code]](https://github.com/affinelayer/pix2pix-tensorflow)\n### Semantic Segmentation Using Adversarial Networks [[paper]](https://arxiv.org/abs/1611.08408)\n### StackGAN Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.03242) [[code]](https://github.com/hanzhanggit/StackGAN)\n### Photo-realistic Single Image Super-resolution Using a Generative Adversarial Network [[paper]](https://arxiv.org/abs/1609.04802) [[tf+tl code]](https://github.com/zsdonghao/SRGAN) [[Torch code]](https://github.com/junhocho/SRGAN) [[Torch code]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)\n### Generating Images with Perceptual Similarity Metrics based on Deep Networks [[paper]](https://arxiv.org/abs/1602.02644)\n### Generating Images with Recurrent Adversarial Networks [[paper]](https://arxiv.org/abs/1602.05110)\n### Generative Visual Manipulation on the Natural Image Manifold [[paper]](https://arxiv.org/abs/1609.03552) [[code]](https://github.com/junyanz/iGAN) [[project]](https://people.eecs.berkeley.edu/~junyanz/projects/gvm/) [[video]](https://www.youtube.com/watch?v=9c4z6YsBGQ0)\n### Generative Adversarial Text to Image Synthesis [[paper]](https://arxiv.org/abs/1605.05396)\n### Learning What and Where to Draw [[paper]](https://arxiv.org/abs/1610.02454)\n### Adversarial Training for Sketch Retrieval [[paper]](https://arxiv.org/abs/1607.02748)\n### Generative Image Modeling using Style and Structure Adversarial Networks [[paper]](https://arxiv.org/abs/1603.05631)\n### Generative Adversarial Networks as Variational Training of Energy Based Models [[paper]](https://arxiv.org/abs/1611.01799)\n### Adversarial Training Methods for Semi-Supervised Text Classification [[paper]](https://arxiv.org/abs/1605.07725)\n### Learning from Simulated and Unsupervised Images through Adversarial Training [[paper]](https://arxiv.org/abs/1612.07828)\n### Synthesizing the preferred inputs for neurons in neural networks via deep generator networks [[paper]](https://arxiv.org/abs/1605.09304)\n### Adversarial Feature Learning [[paper]](https://arxiv.org/abs/1605.09782)\n### Semantic Image Inpainting with Perceptual and Contextual Losses [[paper]](https://arxiv.org/abs/1607.07539)\n#### \"Semantic Image Inpainting with Deep Generative Models\" the same as the above but different title.\n### Context Encoders: Feature Learning by Inpainting [[paper]](https://arxiv.org/abs/1604.07379)\n### Semi-supervised Learning with Context-conditional Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1611.06430)\n### Robust LSTM-autoencoders for Face De-occlusion in the Wild [[paper]](https://arxiv.org/abs/1612.08534)\n### C-RNN-GAN: Continuous Recurrent Neural Networks with Adversarial Training [[paper]](https://arxiv.org/abs/1611.09904)\n### InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [[Paper]](https://arxiv.org/abs/1606.03657)\n### Conditional Image Synthesis with Auxiliary Classifier GANs [[paper]](https://arxiv.org/abs/1610.09585)\n### Pixel-level Domain Transfer [[paper]](https://arxiv.org/abs/1603.07442)\n### Invertible Conditional GANs for Image Editing [[paper]](https://arxiv.org/abs/1611.06355)\n### Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space [[Paper]](https://arxiv.org/abs/1612.00005)\n### Unsupervised Learning for Physical Interaction through Video Prediction [[paper]](https://arxiv.org/abs/1605.07157)\n### Generating Videos with Scene Dynamics [[paper]](https://arxiv.org/abs/1609.02612)\n### Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1604.04382) [[code]](https://github.com/chuanli11/MGANs)\n### Medical Image Synthesis with Context-aware Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.05362)\n### Learning Temporal Transformations from Time-lapse Videos [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46484-8_16)\n### Improved Techniques for Training GANs NIPS 2016 [[paper]](https://papers.nips.cc/paper/6125-improved-techniques-for-training-gans)\n### Mode Regularized Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.02136)\n### Amortised MAP Inference for Image Super-resolution [[paper]](https://openreview.net/pdf?id=S1RP6GLle)\n### Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts\n### Adversarially Learned Inference [[paper]](https://openreview.net/pdf?id=B1ElR4cgg) [[code]](https://github.com/IshmaelBelghazi/ALI)\n### Adversarial Feature Learning [[paper]](https://arxiv.org/abs/1605.09782)\n### EnhanceNet Single Image Super-Resolution through Automated Texture Synthesis [[paper]](https://arxiv.org/abs/1612.07919)\n### One-to-Many Network for Visually Pleasing Compression Artifacts Reduction [[paper]](https://arxiv.org/abs/1611.04994)\n### Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [[paper]](https://arxiv.org/abs/1612.02177) [[Torch code]](https://github.com/SeungjunNah/DeepDeblur_release)\n### Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.05424)\n### Least Squares Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1611.04076) [[code]](https://github.com/xudonmao/LSGAN)\n### Inverting The Generator Of A Generative Adversarial Network [[paper]](https://arxiv.org/abs/1611.05644)\n### Stacked Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.04357)\n### Unrolled Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1611.02163)\n### Energy-based Generative Adversarial Network [[paper]](https://arxiv.org/abs/1609.03126)\n### Neural Photo Editing with Introspective Adversarial Networks [[paper]](https://arxiv.org/abs/1609.07093)\n### Ultra-Resolving Face Images by Discriminative Generative Networks [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46454-1_20)\n### Generative Adversarial Nets from a Density Ratio Estimation Perspective [[paper]](https://arxiv.org/abs/1610.02920)\n### Adversarially Learned Inference [[paper]](https://arxiv.org/abs/1606.00704)\n### Generative Multi-Adversarial Networks [[paper]](https://arxiv.org/abs/1611.01673)\n### (DTN) Unsupervised Cross-Domain Image Generation ICLR 2017 [[paper]](https://arxiv.org/abs/1611.02200) [[code]](https://github.com/yunjey/domain-transfer-network)\n\n## 2017\n### SalGAN: Visual Saliency Prediction with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1701.01081)\n### AdaGAN: Boosting Generative Models [[paper]](https://arxiv.org/abs/1701.02386)\n### Unsupervised Image-to-image Translation with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1701.02676)\n### Adversarial Networks for the Detection of Aggressive Prostate Cancer [[paper]](https://arxiv.org/abs/1702.08014)\n### Generative Adversarial Residual Pairwise Networks for One Shot Learning [[paper]](https://arxiv.org/abs/1703.08033)\n### GP-GAN Towards Realistic High-resolution Image Blending [[paper]](https://arxiv.org/abs/1703.07195)\n### Image De-raining Using a Conditional Generative Adversarial Network [[paper]](https://arxiv.org/abs/1701.05957), [[code]](https://github.com/hezhangsprinter/ID-CGAN)\n### Semi-latent GAN Learning to Generate and Modify Facial Images from Attributes [[paper]](https://arxiv.org/abs/1704.02166)\n### Loss-sensitive Generative Adversarial Networks on Lipschitz Densities [[paper]](https://arxiv.org/abs/1701.06264) [[code]](https://github.com/guojunq/lsgan)\n### On the Effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1704.03971)\n### Beyond Face Rotation Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis [[paper]](https://arxiv.org/abs/1704.04086)\n### Neural Face Editing with Intrinsic Image Disentangling [[paper]](https://arxiv.org/abs/1704.04131)\n### (DualGAN) DualGAN: Unsupervised Dual Learning for Image-to-Image Translation ICCV 2017 [[paper]](https://arxiv.org/abs/1704.02510) [[code]](https://github.com/duxingren14/DualGAN)\n### (CycleGAN) Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks ICCV 2017 [[paper]](https://arxiv.org/abs/1703.10593) [[code]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)\n### Face Aging With Conditional Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1702.01983)\n### Outline Colorization through Tandem Adversarial Networks [[paper]](https://arxiv.org/abs/1704.08834)\n### Real-Time Adaptive Image Compression [[paper]](https://arxiv.org/abs/1705.05823)\n### Improved Training of Wasserstein GANs NIPS 2017 [[paper]](https://arxiv.org/abs/1704.00028) [[code]](https://github.com/igul222/improved_wgan_training)\n### The Cramer Distance as a Solution to Biased Wasserstein Gradients [[paper]](https://arxiv.org/abs/1705.10743) [[code]](https://github.com/jiamings/cramer-gan)\n### Video Imagination from a Single Image with Transformation Generation [[paper]](https://arxiv.org/abs/1706.04124)\n### Perceptual Generative Adversarial Networks for Small Object Detection [[paper]](https://arxiv.org/abs/1706.05274)\n### (TextureGAN) TextureGAN Controlling Deep Image Synthesis with Texture Patches CVPR 2018 [[paper]](https://arxiv.org/abs/1706.02823) [[code]](https://github.com/janesjanes/Pytorch-TextureGAN)\n### Interactive 3D Modeling with a Generative Adversarial Network [[paper]](https://arxiv.org/abs/1706.05170)\n### Bayesian Conditional Generative Adverserial Networks [[paper]](https://arxiv.org/abs/1706.05477)\n### Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis [[paper]](https://arxiv.org/abs/1706.08789)\n### Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1706.09318) [[Keras+TF code]](https://bitbucket.org/woalsdnd/v-gan)\n### Perceptual Adversarial Networks for Image-to-Image Transformation [[paper]](https://arxiv.org/abs/1706.09138)\n### Adversarial Image Alignment and Interpolation [[paper]](https://arxiv.org/abs/1707.00067)\n### On Unifying Deep Generative Models [[paper]](https://arxiv.org/abs/1706.00550)\n### Generalization and Equilibrium in Generative Adversarial Nets (GANs) [[paper]](https://arxiv.org/abs/1703.00573)\n### SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[paper]](https://arxiv.org/abs/1706.01805)\n### Self Adversarial Training for Human Pose Estimation [[paper]](https://arxiv.org/abs/1707.02439)\n### Automatic Liver Segmentation Using an Adversarial Image-to-Image Network [[paper]](https://arxiv.org/abs/1707.08037)\n### AlignGAN Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1707.01400)\n### High-Quality Face Image SR Using Conditional Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1707.00737)\n### Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1707.05392)\n### Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results [[paper]](https://arxiv.org/abs/1707.09585)\n### Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) [[paper]](https://arxiv.org/abs/1707.09747)\n### Deep MR to CT Synthesis using Unpaired Data [[paper]](https://arxiv.org/abs/1708.01155)\n### Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[paper]](https://arxiv.org/abs/1708.00961)\n### Bayesian GAN [[paper]](https://arxiv.org/abs/1705.09558)\n### Stabilizing Training of Generative Adversarial Networks through Regularization [[paper]](https://arxiv.org/abs/1705.09367)\n### GANs for Biological Image Synthesis [[paper]](https://arxiv.org/abs/1708.04692)\n### Sharpness-aware Low dose CT denoising using conditional generative adversarial network [[paper]](https://arxiv.org/abs/1708.06453)\n### Adversarial Generation of Training Examples for Vehicle License Plate Recognition [[paper]](https://arxiv.org/abs/1707.03124)\n### Adversarial Training and Dilated Convolutions for Brain MRI Segmentation [[paper]](https://arxiv.org/abs/1707.03195)\n### Aesthetic-Driven Image Enhancement by Adversarial Learning [[paper]](https://arxiv.org/abs/1707.05251)\n### Adversarial Nets with Perceptual Losses for Text-to-image Synthesis [[paper]](https://arxiv.org/abs/1708.09321)\n### Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network [[paper]](https://arxiv.org/abs/1708.09105)\n### Adversarial PoseNet A Structure-aware Convolutional Network for Human Pose Estimation [[paper]](https://arxiv.org/abs/1705.00389)\n### Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB [[paper]](https://arxiv.org/abs/1709.00265)\n### ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network [[paper]](https://arxiv.org/abs/1709.00938)\n### Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1709.00753)\n### Synthetic Medical Images from Dual Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1709.01872)\n### Learning Loss for Knowledge Distillation with Conditional Adversarial Networks [[papaer]](https://arxiv.org/abs/1709.00513)\n### Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification [[paper]](https://arxiv.org/abs/1709.01703)\n### Towards Understanding Adversarial Learning for Joint Distribution Matching [[paper]](https://arxiv.org/abs/1709.01215)\n### Generative Semantic Manipulation with Contrasting GAN ECCV 2018 [[paper]](https://arxiv.org/abs/1708.00315)\n### Deep Generative Filter for Motion Deblurring [[paper]](https://arxiv.org/abs/1709.03481)\n### Improving Heterogeneous Face Recognition with Conditional Adversarial Networks [[paper]](https://arxiv.org/abs/1709.02848)\n### Exposure A White-Box Photo Post-Processing Framework [[paper]](https://arxiv.org/abs/1709.09602)\n### A Study of Cross-domain Generative Models applied to Cartoon Series [[paper]](https://arxiv.org/abs/1710.00755)\n### Fisher GAN [[paper]](https://arxiv.org/abs/1705.09675)\n### Neural Stain-Style Transfer Learning using GAN for Histopathological Images [[paper]](https://arxiv.org/abs/1710.08543)\n### Many Paths to Equilibrium GANs Do Not Need to Decrease aDivergence At Every Step [[paper]](https://arxiv.org/abs/1710.08446)\n### Progressive Growing of GANs for Improved Quality, Stability, and Variation [[paper]](http://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of//karras2017gan-paper.pdf) [[code]](https://github.com/tkarras/progressive_growing_of_gans)\n### Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1709.08810)\n### Generative Adversarial Network based on Resnet for Conditional Image Restoration [[paper]](https://arxiv.org/abs/1707.04881)\n### Face Transfer with Generative Adversarial Network [[paper]](https://arxiv.org/abs/1710.06090)\n### SEGAN Speech Enhancement Generative Adversarial Network [[paper]](https://arxiv.org/abs/1703.09452) [[code]](https://github.com/santi-pdp/segan)\n### BEGAN Boundary Equilibrium Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1703.10717)\n### StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation CVPR 2018 [[paper]](https://arxiv.org/abs/1711.09020) [[Pytorch code]](https://github.com/yunjey/StarGAN)\n### (pix2pix-HD) High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs CVPR 2018 [[paper]](https://arxiv.org/abs/1711.11585) [[code]](https://github.com/NVIDIA/pix2pixHD)\n### Precise Recovery of Latent Vectors from Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1702.04782)\n### Towards Principled Methods for Training Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1701.04862)\n### Wasserstein GAN [[paper]](https://arxiv.org/abs/1701.07875)\n### The Numerics of GANs [[paper]](https://arxiv.org/abs/1705.10461)\n### Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Hallucinating_Very_Low-Resolution_CVPR_2017_paper.html)\n### Deep Future Gaze Gaze Anticipation on Egocentric Videos Using Adversarial Networks [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Deep_Future_Gaze_CVPR_2017_paper.pdf)\n### DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data [[paper]](https://arxiv.org/abs/1706.02071)\n### MARTA GANs Unsupervised Representation Learning for Remote Sensing Image Classification [[paper]](http://ieeexplore.ieee.org/document/8059820/)\n### Adversarial Variational Bayes Unifying Variational Autoencoders and Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1701.04722)\n### Adversarial Stain Transfer for Histopathology Image Analysis [[paper]](http://ieeexplore.ieee.org/document/8170242/)\n### Geometrical Insights for Implicit Generative Modeling [[paper]](https://arxiv.org/abs/1712.07822)\n\"Leon Bottou\"\n### Multi-Agent Diverse Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1704.02906)\n### Learning Face Age Progression: A Pyramid Architecture of GANs [[paper]](https://arxiv.org/abs/1711.10352)\n### (BicycleGAN) Toward Multimodal Image-to-Image Translation NIPS 2017 [[paper]](https://arxiv.org/abs/1711.11586) [[code]](https://github.com/junyanz/BicycleGAN)\n### (UNIT) Unsupervised Image-to-Image Translation Networks NIPS 2017 [[paper]](https://arxiv.org/abs/1703.00848) [[code]](https://github.com/mingyuliutw/UNIT)\n### (DiscoGAN) Learning to Discover Cross-Domain Relations with Generative Adversarial Networks ICML 2017 [[paper]](https://arxiv.org/abs/1703.05192) [[code]](https://github.com/SKTBrain/DiscoGAN)\n### (DistanceGAN) One-Sided Unsupervised Domain Mapping NISP 2017 [[paper]](https://arxiv.org/abs/1706.00826) [[code]](https://github.com/sagiebenaim/DistanceGAN)\n### (Triangle GAN) Triangle Generative Adversarial Networks NIPS 2017 [[paper]](https://arxiv.org/abs/1709.06548) [[code]](https://github.com/LiqunChen0606/Triangle-GAN)\n### AttGAN: Facial Attribute Editing by Only Changing What You Want 2017 [[paper]](https://arxiv.org/abs/1711.10678) [[code]](https://github.com/LynnHo/AttGAN-Tensorflow)\n\n## 2018\n### Coupled Generative Adversarial Stacked Auto-encoder: CoGASA [[paper]](https://www.sciencedirect.com/science/article/pii/S0893608018300029)\n### High-throughput, High-resolution Generated Adversarial Network Microscopy [[paper]](https://arxiv.org/abs/1801.07330)\n### Geometry Score A Method For Comparing Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1802.026640) [[code]](https://github.com/geom-score/geometry-score)\n### Mastering Sketching Adversarial Augmentation for Structured Prediction [[paper]](https://arxiv.org/abs/1703.08966)\n\"TOG\"\n### 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network [[paper]](https://arxiv.org/abs/1802.05656)\n### Learning to Adapt Structured Output Space for Semantic Segmentation [[paper]](https://arxiv.org/abs/1802.10349)\n### A Study into the Similarity in Generator and Discriminator in GAN Architecture [[paper]](https://arxiv.org/abs/1802.07401)\n### An Introduction to Image Synthesis with Generative Adversarial Nets [[paper]](https://arxiv.org/abs/1803.04469)\n\"text2image, image2image\"\n### Densely Connected Pyramid Dehazing Network [[paper]](https://arxiv.org/abs/1803.08396), [[code]](https://github.com/hezhangsprinter/DCPDN)\n### 3D Conditional Generative Adversarial Networks for High-quality PET Image Estimation at Low Dose [[paper]](https://www.sciencedirect.com/science/article/pii/S1053811918302507)\n### Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network [[paper]](https://arxiv.org/abs/1803.01417)\n### Spectral Normalization for Generative Adversarial Networks ICLR 2018 [[paper]](https://openreview.net/forum?id=B1QRgziT-) [[code]](https://github.com/pfnet-research/sngan_projection)\n### PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup [[paper]](http://gfx.cs.princeton.edu/pubs/Chang_2018_PAS/index.php)\n### Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network CVPR 2018 [[paper]](https://arxiv.org/abs/1802.09655)\n### Improving Shape Deformation in Unsupervised Image-to-Image Translation ECCV 2018 [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Aaron_Gokaslan_Improving_Shape_Deformation_ECCV_2018_paper.html) [[code]](https://github.com/brownvc/ganimorph/)\n### To learn image super-resolution, use a GAN to learn how to do image degradation first ECCV 2018 [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Adrian_Bulat_To_learn_image_ECCV_2018_paper.html)\n### GANimation Anatomically-aware Facial Animation from a Single Image ECCV 2018 [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Albert_Pumarola_Anatomically_Coherent_Facial_ECCV_2018_paper.html) [[code]](https://github.com/albertpumarola/GANimation)\n### SwapNet Garment Transfer in Single View Images ECCV 2018 [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Amit_Raj_SwapNet_Garment_Transfer_ECCV_2018_paper.html)\n### Modular Generative Adversarial Networks ECCV 2018 [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/html/Bo_Zhao_Modular_Generative_Adversarial_ECCV_2018_paper.html)\n### Image Generation from Sketch Constraint Using Contextual GAN ECCV 2018 [[paper]](https://arxiv.org/abs/1711.08972)\n### Smart, Sparse Contours to Represent and Edit Images CVPR 2018 [[paper]](https://arxiv.org/abs/1712.08232)\n### Image-to-image Translation for Cross-domain Disentanglement NIPS 2018 [[paper]](https://arxiv.org/abs/1805.09730)\n### (CartoonGAN) CartoonGAN: Generative Adversarial Networks for Photo Cartoonization CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf) [[code]](https://github.com/znxlwm/pytorch-CartoonGAN)\n### (SCAN) Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks ECCV 2018 [[paper]](https://arxiv.org/abs/1807.08536)\n### (InstaGAN) Instance-aware image-to-image translation 2018 [[paper]](https://openreview.net/pdf?id=ryxwJhC9YX)\n### Geometry Guided Adversarial Facial Expression Synthesis ACM MM 2018 [[paper]](https://arxiv.org/abs/1712.03474)\n### DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks CVPR 2018 [[paper]](https://arxiv.org/abs/1802.06454)\n### Attention-GAN for Object Transfiguration in Wild Images 2018 [[paper]](https://arxiv.org/abs/1803.06798)\n### Unsupervised Attention-guided Image to Image Translation NIPS 2018 [[paper]](https://arxiv.org/abs/1806.02311) [[code]](https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation)\n### Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention 2018 [[paper]](https://arxiv.org/abs/1806.06195)\n### Conditional CycleGAN for Attribute Guided Face Image Generation ECCV 2018 [[paper]](https://arxiv.org/abs/1705.09966)\n### Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data ICML 2018 [[paper]](https://arxiv.org/abs/1802.10151) [[code]](https://github.com/aalmah/augmented_cyclegan)\n### Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation ACM MM 2018 [[paper]](https://arxiv.org/abs/1805.07509) [[code]](https://github.com/zhangqianhui/Sparsely-Grouped-GAN)\n### XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings ICML 2018 [[paper]](https://arxiv.org/abs/1711.05139)\n### ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes ECCV 2018 [[paper]](https://arxiv.org/abs/1803.10562) [[code]](https://github.com/Prinsphield/ELEGANT)\n### (MUNIT) Multimodal Unsupervised Image-to-Image Translation ECCV 2018 [[paper]](https://arxiv.org/abs/1804.04732) [[code]](https://github.com/NVlabs/MUNIT)\n### Conditional Image-to-Image Translation CVPR 2018 [[paper]](https://arxiv.org/abs/1805.00251)\n### Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency\n### Diverse Image-to-Image Translation via Disentangled Representations ECCV 2018 [[paper]](https://arxiv.org/abs/1808.00948) [[code]](https://github.com/HsinYingLee/DRIT)\n### (BigGANs) Large Scale GAN Training for High Fidelity Natural Image Synthesis ICLR 2019 [[paper]](https://arxiv.org/abs/1809.11096)\n\n## 2019\n### Self-Supervised Generative Adversarial Networks CVPR 2019 [[paper]](https://arxiv.org/abs/1811.11212)\n”arXiv v2: Self-Supervised GANs via Auxiliary Rotation Loss“\n### SinGAN: Learning a Generative Model from a Single Natural Image ICCV 2019 [[paper]](https://arxiv.org/abs/1905.01164)\n"
  }
]