{"id":13444438,"url":"https://github.com/pshams55/GAN-Case-Study","last_synced_at":"2025-03-20T18:32:42.794Z","repository":{"id":45673575,"uuid":"216936725","full_name":"pshams55/GAN-Case-Study","owner":"pshams55","description":null,"archived":false,"fork":false,"pushed_at":"2023-02-08T04:46:59.000Z","size":313,"stargazers_count":121,"open_issues_count":1,"forks_count":21,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-08-01T04:02:09.304Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pshams55.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-10-23T00:50:40.000Z","updated_at":"2024-07-31T13:11:07.000Z","dependencies_parsed_at":"2024-01-13T22:54:59.691Z","dependency_job_id":"e9fa490e-6af5-4c7b-b20b-0c2625d009a4","html_url":"https://github.com/pshams55/GAN-Case-Study","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pshams55%2FGAN-Case-Study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pshams55%2FGAN-Case-Study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pshams55%2FGAN-Case-Study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pshams55%2FGAN-Case-Study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pshams55","download_url":"https://codeload.github.com/pshams55/GAN-Case-Study/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221792892,"owners_count":16881289,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T04:00:22.959Z","updated_at":"2024-10-28T06:30:57.798Z","avatar_url":"https://github.com/pshams55.png","language":null,"funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# GAN-Case-Study\nA list of papers and other on Generative Adversarial Networks.\nThis site is maintained by Pourya Shamsolmoali.\n\nIf you find this work useful for your research, please cite our paper:\n\n```\n@article{shamsolmoali2020image,\n  title={Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies},\n  author={Shamsolmoali, Pourya and Zareapoor, Masoumeh and Granger, Eric and Zhou, Huiyu and Wang, Ruili and Celebi, M Emre and Yang, Jie},\n  journal={Information Fusion},\n  year={2020}\n}\n```\n\n**NOTE:** This site will be updated on monthly basis.\n\n## Contents\n- [Codes](#code)\n- [Datasets](#datasets)\n- [Papers](#papers)\n  - [Overview](#overview)\n  - [Theory \u0026 Machine Learning](#theory--machine-learning)\n  - [Applied Vision](#applied-vision)\n  - [Image Fusion](#image-fusion)\n  - [Medical Images](#medical-images)\n  - [Applied Other](#applied-other)\n  \n\n# Codes\n- Cleverhans: A library for benchmarking vulnerability to adversarial examples [[Code]](https://github.com/openai/cleverhans) [[Blog]](http://cleverhans.io/)\n- CycleGAN [[Code]](https://github.com/junyanz/CycleGAN/) [[Blog]](https://junyanz.github.io/CycleGAN/)\n- Cartoon Face Generation [[Code]](https://github.com/hsiehjackson/Cartoon-Face-Generation)\n- DCGAN [[Code]](https://github.com/mitchelljy/DCGAN-Keras)\n- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) [[Blog]](https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f) [[Code]](https://github.com/devnag/pytorch-generative-adversarial-networks)\n- Generative Models: Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow [[Code]](https://github.com/wiseodd/generative-models)\n- Info-GAN [[Code]](https://github.com/JonathanRaiman/tensorflow-infogan)\n- Keras-GAN [[Code]](https://github.com/eriklindernoren/Keras-GAN)\n- PyTorch-GAN [[Code]](https://github.com/eriklindernoren/PyTorch-GAN)\n- StarGAN [[Code]](https://github.com/yunjey/stargan)\n- StarGAN-v2 [[Code]](https://github.com/clovaai/stargan-v2)\n- Tensorflow-GAN [[Code]](https://github.com/ckmarkoh/GAN-tensorflow) [[Code]](https://github.com/carpedm20/DCGAN-tensorflow)\n- 3D Object Reconstruction [[Code]](https://github.com/Yang7879/3D-RecGAN) [[Code]](https://github.com/autonomousvision/differentiable_volumetric_rendering)\n\n# Datasets\n- Animal Faces-HQ dataset (AFHQ) [[Dataset]](https://www.kaggle.com/andrewmvd/animal-faces)\n- CartoonSet [[Dataset]](https://google.github.io/cartoonset/)\n- Caltech-UCSD Birds-200-2011 [[Dataset]](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)\n- CelebA [[Dataset]](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)\n- CIFAR-10 and CIFAR-100 [[Dataset]](https://www.cs.toronto.edu/~kriz/cifar.html)\n- Cityscapes [[Dataset]](https://www.cityscapes-dataset.com/)\n- DSLR [[Dataset]](https://cvl.tuwien.ac.at/research/cvl-databases/pcb-dslr-dataset/)\n- FaceScrub [[Dataset]](http://vintage.winklerbros.net/facescrub.html)\n- Fashion MNIST [[Dataset]](https://www.kaggle.com/zalando-research/fashionmnist)\n- Flickr-Faces-HQ (FFHQ) [[Dataset]](https://www.kaggle.com/greatgamedota/ffhq-face-data-set), [[Dataset]](https://github.com/NVlabs/ffhq-dataset)\n- Generative Dog Images [[Dataset]](https://www.kaggle.com/c/generative-dog-images?rvi=1)\n- ImageNet [[Dataset]](http://www.image-net.org/)\n- MNIST [[Datase]](http://yann.lecun.com/exdb/mnist/)\n- Paris StreetView [[Dataset]](https://www.crcv.ucf.edu/projects/GMCP_Geolocalization/), [[Dataset]](https://www.crcv.ucf.edu/projects/GMCP_Geolocalization/)\n- SENSIAC [[Dataset]](https://blogs.upm.es/gti-work/2013/05/06/sensiac-dataset-for-automatic-target-recognition-in-infrared-imagery/)\n- Toronto Faces [[Dataset]](https://www.kaggle.com/general/50987)\n- UT Zappos50K [[Dataset]](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/)\n- Van Gogh [[Dataset]](https://www.kaggle.com/gfolego/vangogh)\n- YouTubeFace [[Dataset]](https://www.cs.tau.ac.il/~wolf/ytfaces/)\n\n# Papers\n## Overview\n- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [[arXiv]](https://arxiv.org/abs/2001.06937)\n- A Review: Generative Adversarial Networks [[paper]](https://ieeexplore.ieee.org/abstract/document/8833686)\n- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [[arXiv]](https://arxiv.org/abs/2001.06937)\n- Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy [[arXiv]](https://arxiv.org/abs/1906.01529)\n- Generative Adversarial Network (GAN): a general review on different variants of GAN and applications [[paper]](https://ieeexplore.ieee.org/document/9489160)\n- Generative Adversarial Networks: An Overview [[arXiv]](https://arxiv.org/abs/1710.07035)\n- Generative Adversarial Network in Medical Imaging: A Review [[arXiv]](https://arxiv.org/abs/1809.07294)\n- Stabilizing Generative Adversarial Networks: A Survey [[arXiv]](https://arxiv.org/abs/1910.00927)\n\n## Theory \u0026 Machine Learning\n- Adversarial Feature Learning [[arXiv]](https://arxiv.org/abs/1605.09782v7)\n- A Classification-Based Perspective on GAN Distributions [[arXiv]](https://arxiv.org/abs/1711.00970)\n- A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models [[arXiv]](https://arxiv.org/abs/1611.03852)\n- A General Retraining Framework for Scalable Adversarial Classification [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_2.pdf)\n- Activation Maximization Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1703.02000)\n- AdaGAN: Boosting Generative Models [[arXiv]](https://arxiv.org/abs/1701.02386)\n- Adversarial Autoencoders [[arXiv]](https://arxiv.org/abs/1511.05644)\n- Adversarial Discriminative Domain Adaptation [[arXiv]](https://arxiv.org/abs/1702.05464)\n- Adversarial Distillation of Bayesian Neural Network Posteriors [[arXiv]](https://arxiv.org/abs/1806.10317)\n- Adversarial Generator-Encoder Networks [[arXiv]](https://arxiv.org/pdf/1704.02304.pdf)\n- Adversarial Feature Learning [[arXiv]](https://arxiv.org/abs/1605.09782) [[Code]](https://github.com/wiseodd/generative-models)\n- Adversarially Learned Inference [[arXiv]](https://arxiv.org/abs/1606.00704) [[Code]](https://github.com/wiseodd/generative-models)\n- AE-GAN: adversarial eliminating with GAN [[arXiv]](https://arxiv.org/abs/1707.05474)\n- AmbientGAN: Generative models from lossy measurements [[arXiv]](https://openreview.net/forum?id=Hy7fDog0b)\n- An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks [[arXiv]](https://arxiv.org/abs/1702.02382)\n- Annealed Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1705.07505)\n- APE-GAN: Adversarial Perturbation Elimination with GAN [[arXiv]](https://arxiv.org/abs/1707.05474)\n- Associative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.06953)\n- Autoencoding beyond pixels using a learned similarity metric [[arXiv]](https://arxiv.org/abs/1512.09300)\n- Automatic Steganographic Distortion Learning Using a Generative Adversarial Network [[paper]](https://ieeexplore.ieee.org/document/8017430)\n- AVID: Adversarial Visual Irregularity Detection [[arXiv]](https://arxiv.org/abs/1805.09521)\n- BAGAN: Data Augmentation with Balancing GAN [[arXiv]](https://arxiv.org/abs/1803.09655)\n- BinGAN: Learning Compact Binary Descriptors with a Regularized GAN [[arXiv]](https://arxiv.org/abs/1806.06778)\n- BourGAN: Generative Networks with Metric Embeddings [[arXiv]](https://arxiv.org/abs/1805.07674)\n- Bayesian Conditional Generative Adverserial Networks [[arXiv]](https://arxiv.org/abs/1706.05477)\n- Bayesian GAN [[arXiv]](https://arxiv.org/abs/1705.09558)\n- BEGAN: Boundary Equilibrium Generative Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_4.pdf) [[arXiv]](https://arxiv.org/abs/1703.10717) [[Code]](https://github.com/wiseodd/generative-models)\n- Binary Generative Adversarial Networks for Image Retrieval [[arXiv]](https://arxiv.org/abs/1708.04150)\n- Boundary-Seeking Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.08431) [[Code]](https://github.com/wiseodd/generative-models)\n- Calibrating Energy-based Generative Adversarial Networks [[arXiv]](https://arxiv.org/pdf/1702.01691.pdf)\n- CapsGAN: Using Dynamic Routing for Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1806.03968)\n- CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training [[arXiv]](https://arxiv.org/abs/1709.02023)\n- Class-Splitting Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.07359)\n- Comparison of Maximum Likelihood and GAN-based training of Real NVPs [[arXiv]](https://arxiv.org/abs/1705.05263)\n- Conditional CycleGAN for Attribute Guided Face Image Generation [[arXiv]](https://arxiv.org/abs/1705.09966)\n- Conditional Infilling GANs for Data Augmentation in Mammogram Classification [[arXiv]](https://arxiv.org/abs/1807.08093)\n- Conditional Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1411.1784) [[Code]](https://github.com/wiseodd/generative-models)\n- Connecting Generative Adversarial Networks and Actor-Critic Methods [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_1.pdf)\n- Continual Learning in Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1705.08395)\n- C-RNN-GAN: Continuous recurrent neural networks with adversarial training [[arXiv]](https://arxiv.org/abs/1611.09904)\n- CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning [[arXiv]](https://arxiv.org/abs/1710.05106)\n- Cooperative Training of Descriptor and Generator Networks [[arXiv]](https://arxiv.org/abs/1609.09408)\n- Coupled Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1606.07536) [[Code]](https://github.com/wiseodd/generative-models)\n- DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning [[arXiv]](https://arxiv.org/abs/2006.08694)\n- Differentiable Augmentation for Data-Efficient GAN Training [[arXiv]](https://arxiv.org/abs/2006.10738)\n- Diverse Image Generation via Self-Conditioned GANs [[arXiv]](https://arxiv.org/abs/2006.10728)\n- Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN [[arXiv]](https://arxiv.org/abs/1808.01960)\n- Dualing GANs [[arXiv]](https://arxiv.org/abs/1706.06216)\n- Deep and Hierarchical Implicit Models [[arXiv]](https://arxiv.org/abs/1702.08896)\n- Energy-based Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1609.03126) [[Code]](https://github.com/wiseodd/generative-models)\n- Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1803.08182)\n- Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs [[paper]](https://arxiv.org/pdf/1810.04147.pdf)\n- Explaining and Harnessing Adversarial Examples [[arXiv]](https://arxiv.org/abs/1412.6572)\n- Hierarchical Implicit Models and Likelihood-Free Variational Inference [[arXiv]](https://arxiv.org/abs/1702.08896)\n- Flow-GAN: Bridging implicit and prescribed learning in generative models [[arXiv]](https://arxiv.org/abs/1705.08868)\n- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization [[arXiv]](https://arxiv.org/abs/1606.00709) [[Code]](https://github.com/wiseodd/generative-models)\n- GAN Memory with No Forgetting [[arXiv]](https://arxiv.org/abs/2006.07543)\n- GAN Dissection: Visualizing and understanding generative adversarial networks [[arXiv]](https://openreview.net/pdf?id=Hyg_X2C5FX)\n- Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking [[arXiv]](https://arxiv.org/abs/1704.04865)\n- Generalization and Equilibrium in Generative Adversarial Nets (GANs) [[arXiv]](https://arxiv.org/abs/1703.00573)\n- Generating images with recurrent adversarial networks [[arXiv]](https://arxiv.org/abs/1602.05110)\n- Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1406.2661) [[Code]](https://github.com/goodfeli/adversarial) [[Code]](https://github.com/wiseodd/generative-models)\n- Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks [[arXiv]](https://arxiv.org/abs/2004.02182)\n- Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation [[arXiv]](https://arxiv.org/abs/1808.05599)\n- Implicit competitive regularization in GANs [[paper]](https://arxiv.org/pdf/1910.05852.pdf)\n- Generalization Properties of Optimal Transport GANs with Latent Distribution Learning [[arXiv]](https://arxiv.org/abs/2007.14641)\n- Generating Adversarial Examples with Adversarial Networks [[arXiv]](https://arxiv.org/abs/1801.02610)\n- Generative Adversarial Networks as Variational Training of Energy Based Models [[arXiv]](https://arxiv.org/abs/1611.01799)\n- Generative Adversarial Networks with Inverse Transformation Unit [[arXiv]](https://arxiv.org/abs/1709.09354)\n- Generative Adversarial Parallelization [[arXiv]](https://arxiv.org/abs/1612.04021) [[Code]](https://github.com/wiseodd/generative-models)\n- Generative Adversarial Residual Pairwise Networks for One Shot Learning [[arXiv]](https://arxiv.org/abs/1703.08033)\n- Generative Adversarial Structured Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_14.pdf)\n- Generative Cooperative Net for Image Generation and Data Augmentation [[arXiv]](https://arxiv.org/abs/1705.02887)\n- Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization [[arXiv]](https://arxiv.org/abs/1809.05972)\n- Generative Moment Matching Networks [[arXiv]](https://arxiv.org/abs/1502.02761) [[Code]](https://github.com/yujiali/gmmn)\n- Generative Semantic Manipulation with Contrasting GAN [[arXiv]](https://arxiv.org/abs/1708.00315)\n- Geometric GAN [[arXiv]](https://arxiv.org/abs/1705.02894)\n- Good Semi-supervised Learning that Requires a Bad GAN [[arXiv]](https://arxiv.org/abs/1705.09783)\n- Gradient descent GAN optimization is locally stable [[arXiv]](https://arxiv.org/abs/1706.04156)\n- How to Train Your DRAGAN [[arXiv]](https://arxiv.org/abs/1705.07215)\n- Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.02237)\n- Improved Semi-supervised Learning with GANs using Manifold Invariances [[arXiv]](https://arxiv.org/abs/1705.08850)\n- Improved Techniques for Training GANs [[arXiv]](https://arxiv.org/abs/1606.03498) [[Code]](https://github.com/openai/improved-gan)\n- Improved Training of Wasserstein GANs [[arXiv]](https://arxiv.org/abs/1704.00028) [[Code]](https://github.com/wiseodd/generative-models)\n- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1606.03657) [[Code]](https://github.com/wiseodd/generative-models)\n- Inverting The Generator Of A Generative Adversarial Network [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_9.pdf)\n- Instance Selection for GANs [[arXiv]](https://arxiv.org/abs/2007.15255)\n- It Takes (Only) Two: Adversarial Generator-Encoder Networks [[arXiv]](https://arxiv.org/abs/1704.02304)\n- KGAN: How to Break The Minimax Game in GAN [[arXiv]](https://arxiv.org/abs/1711.01744)\n- Learn distributed GAN with Temporary Discriminators [[arXiv]](https://arxiv.org/abs/2007.09221)\n- Learning in Implicit Generative Models [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_10.pdf)\n- Learning Loss for Knowledge Distillation with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.00513)\n- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05192) [[Code]](https://github.com/wiseodd/generative-models)\n- Latent Space Optimal Transport for Generative Models [[arXiv]](https://arxiv.org/abs/1809.05964)\n- Learning Texture Manifolds with the Periodic Spatial GAN [[arXiv]](https://arxiv.org/abs/1705.06566)\n- Least Squares Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.04076) [[Code]](https://github.com/wiseodd/generative-models)\n- Linking Generative Adversarial Learning and Binary Classification [[arXiv]](https://arxiv.org/abs/1709.01509)\n- Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities [[arXiv]](https://arxiv.org/abs/1701.06264)\n- LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation [[arXiv]](https://arxiv.org/abs/1703.01560)\n- MAGAN: Margin Adaptation for Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.03817) [[Code]](https://github.com/wiseodd/generative-models)\n- Maximum-Likelihood Augmented Discrete Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.07983)\n- McGan: Mean and Covariance Feature Matching GAN [[arXiv]](https://arxiv.org/abs/1702.08398)\n- Message Passing Multi-Agent GANs [[arXiv]](https://arxiv.org/abs/1612.01294)\n- MMD GAN: Towards Deeper Understanding of Moment Matching Network [[arXiv]](https://arxiv.org/abs/1705.08584)\n- Mode Regularized Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.02136) [[Code]](https://github.com/wiseodd/generative-models)\n- Multi-Agent Diverse Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.02906)\n- Multi-Generator Gernerative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1708.02556)\n- Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models [[arXiv]](https://arxiv.org/abs/1705.10843)\n- Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search [[arXiv]](https://arxiv.org/abs/2007.09180)\n- On Convergence and Stability of GANs [[arXiv]](https://arxiv.org/abs/1705.07215)\n- On the Convergence and Robustness of Training GANs with Regularized Optimal Transports [[arXiv]](https://arxiv.org/abs/1802.08249)\n- On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.03971)\n- On the Quantitative Analysis of Decoder-Based Generative Models [[arXiv]](https://arxiv.org/abs/1611.04273)\n- Optimal Transport using GANs for Lineage Tracing  [[arXiv]](https://arxiv.org/abs/2007.12098)\n- Optimizing the Latent Space of Generative Networks [[arXiv]](https://arxiv.org/abs/1707.05776)\n- Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis [[arXiv]](https://arxiv.org/pdf/2008.03071.pdf)\n- Parametrizing filters of a CNN with a GAN [[arXiv]](https://arxiv.org/abs/1710.11386)\n- Private Post-GAN Boosting [[arXiv]](https://arxiv.org/abs/2007.11934)\n- PixelGAN Autoencoders [[arXiv]](https://arxiv.org/abs/1706.00531)\n- Progressive Growing of GANs for Improved Quality, Stability, and Variation [[arXiv]](https://arxiv.org/abs/1710.10196) [[Code]](https://github.com/tkarras/progressive_growing_of_gans)\n- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [[arXiv]](https://arxiv.org/abs/1609.05473)\n- Sobolev GAN [[arXiv]](https://arxiv.org/abs/1711.04894)\n- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1803.10892)\n- SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints [[arXiv]](https://arxiv.org/abs/1806.01482)\n- Simple Black-Box Adversarial Perturbations for Deep Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_11.pdf)\n- Softmax GAN [[arXiv]](https://arxiv.org/abs/1704.06191)\n- Stabilizing Training of Generative Adversarial Networks through Regularization [[arXiv]](https://arxiv.org/abs/1705.09367)\n- Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.04357)\n- Statistics of Deep Generated Images [[arXiv]](https://arxiv.org/abs/1708.02688)\n- Structured Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1711.00889)\n- Tangent-Normal Adversarial Regularization for Semi-supervised Learning [[pdf]](https://arxiv.org/pdf/1808.06088.pdf) [[code]](https://github.com/z331565360/Localized-GAN)\n- Tensorizing Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1710.10772)\n- The Cramer Distance as a Solution to Biased Wasserstein Gradients [[arXiv]](https://arxiv.org/abs/1705.10743)\n- Towards Understanding Adversarial Learning for Joint Distribution Matching [[arXiv]](https://arxiv.org/abs/1709.01215)\n- Triangle Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.06548)\n- Training generative neural networks via Maximum Mean Discrepancy optimization [[arXiv]](https://arxiv.org/abs/1505.03906)\n- Triple Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1703.02291)\n- Unrolled Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.02163)\n- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1511.06434) [[Code]](https://github.com/Newmu/dcgan_code) [[Code]](https://github.com/pytorch/examples/tree/master/dcgan) [[Code]](https://github.com/carpedm20/DCGAN-tensorflow) [[Code]](https://github.com/soumith/dcgan.torch) [[Code]](https://github.com/jacobgil/keras-dcgan)\n- Variational Approaches for Auto-Encoding Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.04987)\n- Variance Regularizing Adversarial Learning [[arXiv]](https://arxiv.org/abs/1707.00309)\n- Wasserstein Distance Guided Representation Learning for Domain Adaptation [[arXiv]](https://arxiv.org/abs/1707.01217)\n- Wasserstein GAN [[arXiv]](https://arxiv.org/abs/1701.07875) [[Code]](https://github.com/martinarjovsky/WassersteinGAN) [[Code]](https://github.com/wiseodd/generative-models)\n\n## Applied Vision\n- 3D Object Reconstruction from a Single Depth View with Adversarial Learning  [[arXiv]](https://arxiv.org/abs/1708.07969)\n- 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations [[arXiv]](https://arxiv.org/abs/1805.00328)\n- 3D Shape Induction from 2D Views of Multiple Objects [[arXiv]](https://arxiv.org/abs/1612.05872)\n- ABC-GAN:Adaptive Blur and Control for improved training stability of Generative Adversarial Networks [[paper]](https://drive.google.com/file/d/0B3wEP_lEl0laVTdGcHE2VnRiMlE/view)\n- Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters [[arXiv]](https://arxiv.org/abs/1705.02355)\n- AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples  [[arXiv]](https://arxiv.org/abs/1805.04680)\n- AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems [[arXiv]](https://arxiv.org/abs/1804.05347)\n- AlphaGAN: Generative adversarial networks for natural image matting [[arXiv]](https://arxiv.org/abs/1807.10088)\n- AMIL: Adversarial Multi-instance Learning for Human Pose Estimation [[arXiv]](https://arxiv.org/abs/2003.08002) [[Code]](https://github.com/pshams55/AMIL)\n- A step towards procedural terrain generation with GANs [[arXiv]](https://arxiv.org/abs/1707.03383) [[Code]](https://github.com/christopher-beckham/gan-heightmaps)\n- Abnormal Event Detection in Videos using Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1708.09644)\n- Adversarial Generation of Training Examples for Vehicle License Plate Recognition [[arXiv]](https://arxiv.org/abs/1707.03124)\n- Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos [[arXiv]](https://arxiv.org/abs/1803.09092)\n- Adversarial nets with perceptual losses for text-to-image synthesis [[arXiv]](https://arxiv.org/abs/1708.09321)\n- Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB [[arXiv]](https://arxiv.org/abs/1709.00265)\n- Adversarial Networks for the Detection of Aggressive Prostate Cancer [[arXiv]](https://arxiv.org/abs/1702.08014)\n- Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation [[arXiv]](https://arxiv.org/pdf/1705.00389.pdf)\n- Adversarial Training For Sketch Retrieval [[arXiv]](https://arxiv.org/abs/1607.02748)\n- Aesthetic-Driven Image Enhancement by Adversarial Learning [[arXiv]](https://arxiv.org/abs/1707.05251)\n- Age Progression / Regression by Conditional Adversarial Autoencoder [[arXiv]](https://arxiv.org/abs/1702.08423)\n- AgingMapGAN (AMGAN): High-ResolutionControllable Face Aging with Spatially-Aware Conditional GANs [[arXiv]](https://arxiv.org/pdf/2008.10960.pdf)\n- Alias-Free Generative Adversarial Networks [[arXiv]](https://arxiv.org/pdf/2106.12423.pdf)\n- AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.01400)\n- Amortised MAP Inference for Image Super-resolution [[arXiv]](https://arxiv.org/abs/1610.04490)\n- Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network [[arXiv]](https://arxiv.org/abs/1811.00344) [[Code]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)\n- A Novel Approach to Artistic Textual Visualization via GAN [[arXiv]](https://arxiv.org/abs/1710.10553)\n- Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification [[arXiv]](https://arxiv.org/abs/1709.03654)\n- A Provably Convergent and Practical Algorithm for Min-max Optimization with Applications to GANs [[arXiv]](https://arxiv.org/abs/2006.12376)\n- Arbitrary Facial Attribute Editing: Only Change What You Want [[arXiv]](https://arxiv.org/abs/1711.10678) [[Code]](https://github.com/LynnHo/AttGAN-Tensorflow)\n- ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1709.00938)\n- ArtGAN: Artwork Synthesis with Conditional Categorial GANs [[arXiv]](https://arxiv.org/abs/1702.03410)\n- Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data [[arXiv]](https://arxiv.org/abs/1711.02010)\n- A Style-Based Generator Architecture for Generative Adversarial Networks [[pdf]](https://arxiv.org/pdf/1812.04948.pdf)\n- Assessing Generative Models via Precision and Recall [[pdf]](https://arxiv.org/pdf/1806.00035.pdf) [[code]](https://github.com/msmsajjadi/precision-recall-distributions.)\n- Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis [[arXiv]](https://arxiv.org/abs/1706.08789)\n- Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1705.01908)\n- Automatic Liver Segmentation Using an Adversarial Image-to-Image Network [[arXiv]](https://arxiv.org/abs/1707.08037)\n- Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis [[arXiv]](https://arxiv.org/abs/1704.04086)\n- Boundary-Seeking Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.08431v1)\n- CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer [[arXiv]](https://arxiv.org/pdf/2008.10298.pdf)\n- C-RNN-GAN: Continuous recurrent neural networks with adversarial training [[arXiv]](https://arxiv.org/abs/1611.09904)\n- CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms [[arXiv]](https://arxiv.org/abs/1706.07068)\n- CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis [[arXiv]](https://arxiv.org/abs/2007.12909)\n- ClusterGAN : Latent Space Clustering in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1809.03627)\n- COCO-GAN: Generation by Parts via Conditional Coordinating [[arXiv]](https://arxiv.org/abs/1904.00284) [[Code]](https://github.com/hubert0527/COCO-GAN)\n- ComboGAN: Unrestrained Scalability for Image Domain Translation [[arXiv]](https://arxiv.org/abs/1712.06909)\n- CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition [[arXiv]](https://arxiv.org/abs/1811.07441) [[Code]](https://github.com/nschor/CompoNet)\n- Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.00753)\n- Conditional Adversarial Network for Semantic Segmentation of Brain Tumor [[arXiv]](https://arxiv.org/abs/1708.05227)\n- Conditional generative adversarial nets for convolutional face generation [[Paper]](http://www.foldl.me/uploads/2015/conditional-gans-face-generation/paper.pdf)\n- Conditional Image Synthesis with Auxiliary Classifier GANs [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_7.pdf) [[arXiv]](https://arxiv.org/abs/1610.09585) [[Code]](https://github.com/wiseodd/generative-models)\n- Contextual RNN-GANs for Abstract Reasoning Diagram Generation [[arXiv]](https://arxiv.org/abs/1609.09444)\n- Controllable Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1708.00598)\n- Correlated discrete data generation using adversarial training [[arXiv]](https://arxiv.org/abs/1804.00925)\n- Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields [[arXiv]](https://arxiv.org/abs/1708.08819)\n- Creatism: A deep-learning photographer capable of creating professional work [[arXiv]](https://arxiv.org/abs/1707.03491)\n- CR-GAN: Learning Complete Representations for Multi-view Generation [[paper]](https://www.ijcai.org/Proceedings/2018/0131.pdf)\n- Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation [[arXiv]](https://arxiv.org/abs/1702.03431)\n- Cross-Domain Face Synthesis using a Controllable GAN [[arXiv]](https://arxiv.org/pdf/1910.14247.pdf)\n- CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training [[arXiv]](https://arxiv.org/abs/1703.10155)\n- Data Augmentation in Classification using GAN [[arXiv]](https://arxiv.org/abs/1711.00648)\n- Deep Generative Adversarial Compression Artifact Removal [[arXiv]](https://arxiv.org/abs/1704.02518)\n- Deep Generative Adversarial Networks for Compressed Sensing (GANCS) Automates MRI [[arXiv]](https://arxiv.org/abs/1706.00051)\n- Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis [[arXiv]](https://arxiv.org/abs/1708.00129)\n- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks [[arXiv]](https://arxiv.org/abs/1506.05751) [[Code]](https://github.com/facebook/eyescream) [[Blog]](http://soumith.ch/eyescream/)\n- Deep multi-scale video prediction beyond mean square error [[arXiv]](https://arxiv.org/abs/1511.05440) [[Code]](https://github.com/dyelax/Adversarial_Video_Generation)\n- Deep Unsupervised Representation Learning for Remote Sensing Images [[arXiv]](https://arxiv.org/abs/1612.08879)\n- DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data [[arXiv]](https://arxiv.org/abs/1706.02071)\n- Depth Structure Preserving Scene Image Generation [[arXiv]](https://arxiv.org/abs/1706.00212)\n- Designing GANs: A Likelihood Ratio Approach [[paper]](https://arxiv.org/pdf/2002.00865.pdf)\n- Detection, Attribution and Localization of GAN Generated Images [[arXiv]](https://arxiv.org/abs/2007.10466)\n- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation [[arXiv]](https://arxiv.org/abs/1704.02510) [[Code]](https://github.com/wiseodd/generative-models)\n- Dual Motion GAN for Future-Flow Embedded Video Prediction [[arXiv]](https://arxiv.org/abs/1708.00284)\n- EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning [[arXiv]](https://arxiv.org/abs/1901.00212) [[Code]](https://github.com/knazeri/edge-connect)\n- Efficient Super Resolution For Large-Scale Images Using Attentional GAN [[arXiv]](https://arxiv.org/abs/1812.04821) [[Thesis]](https://digitalcommons.wpi.edu/etd-theses/1256/) [[Thesis]](https://www.wpi.edu/news/announcements/data-science-ms-thesis-presentation-xiaozhou-zou)\n- Empirical Analysis of Overfitting and Mode Drop in GAN Training [[arXiv]](https://arxiv.org/abs/2006.14265)\n- ExprGAN: Facial Expression Editing with Controllable Expression Intensity [[arXiv]](https://arxiv.org/abs/1709.03842)\n- Face Aging with Contextual Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1802.00237)\n- Free-Form Image Inpainting with Gated Convolution [[arXiv]](https://arxiv.org/abs/1806.03589)\n- Free-Form Image Inpainting with Gated Convolution [[arXiv]](https://arxiv.org/abs/1806.03589)\n- Face Aging With Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.01983)\n- Face Transfer with Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1710.06090)\n- Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1710.04835)\n- Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.05392)\n- From source to target and back: symmetric bi-directional adaptive GAN [[arXiv]](https://arxiv.org/abs/1705.08824)\n- Full Resolution Image Compression with Recurrent Neural Networks [[arXiv]](https://arxiv.org/abs/1608.05148)\n- GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework [[arXiv]](https://arxiv.org/pdf/2008.11062.pdf)\n- G-GANISR: Gradual generative adversarial network for image super resolution [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231219311130)\n- Generative Adversarial Frontal View to Bird View Synthesis [[arXiv]](https://arxiv.org/abs/1808.00327)\n- Generative Modeling by Estimating Gradients of the Data Distribution [[pdf]](https://papers.nips.cc/paper/9361-generative-modeling-by-estimating-gradients-of-the-data-distribution.pdf)\n- GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data [[arXiv]](https://arxiv.org/abs/1705.04932) [[Code]](https://github.com/Prinsphield/GeneGAN)\n- Generate Identity-Preserving Faces by Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.03227)\n- Generate To Adapt: Aligning Domains using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.01705)\n- Generative Adversarial Models for People Attribute Recognition in Surveillance [[arXiv]](https://arxiv.org/abs/1707.02240)\n- Generative Adversarial Network based on Resnet for Conditional Image Restoration [[arxiv]](https://arxiv.org/abs/1707.04881)\n- Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces [[arXiv]](https://arxiv.org/abs/1708.02681)\n- Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking [[arXiv]](https://arxiv.org/abs/1705.05103)\n- Generative Adversarial Text to Image Synthesis [[arXiv]](https://arxiv.org/abs/1605.05396) [[Code]](https://github.com/paarthneekhara/text-to-image)\n- Generative Visual Manipulation on the Natural Image Manifold [[Project]](http://www.eecs.berkeley.edu/~junyanz/projects/gvm/) [[Youtube]](https://youtu.be/9c4z6YsBGQ0) [[Paper]](https://arxiv.org/abs/1609.03552) [[Code]](https://github.com/junyanz/iGAN)\n- Global-to-Local Generative Model for 3D Shapes [[Project]](http://vcc.szu.edu.cn/research/2018/G2L)[[Code]](https://github.com/Hao-HUST/G2LGAN)\n- GEN: Generative Equivariant Networks for diverse image-to-image translation [[Paper]](https://ieeexplore.ieee.org/abstract/document/9770477)\n- GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks [[arXiv]](https://arxiv.org/abs/1710.00962)\n- GP-GAN: Towards Realistic High-Resolution Image Blending [[arXiv]](https://arxiv.org/abs/1703.07195)\n- Guiding InfoGAN with Semi-Supervision [[arXiv]](https://arxiv.org/abs/1707.04487)\n- How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis [[arXiv]](https://arxiv.org/abs/1710.09762)\n- Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1709.07581)\n- High-Quality Face Image SR Using Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.00737)\n- High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks [[arXiv]](https://arxiv.org/abs/1710.10182)\n- Image De-raining Using a Conditional Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1701.05957)\n- Image Generation and Editing with Variational Info Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.04568)\n- Image-to-Image Translation with Text Guidance [[paper]](https://arxiv.org/pdf/2002.05235.pdf)\n- Image-to-Image Translation with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.07004) [[Code]](https://github.com/phillipi/pix2pix)\n- Improved Adversarial Systems for 3D Object Generation and Reconstruction [[arXiv]](https://arxiv.org/abs/1707.09557) [[Code]](https://github.com/EdwardSmith1884/3D-IWGAN)\n- Improving Heterogeneous Face Recognition with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.02848)\n- Improving image generative models with human interactions [[arXiv]](https://arxiv.org/abs/1709.10459)\n- Imitating Driver Behavior with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.06699)\n- Interactive 3D Modeling with a Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1706.05170)\n- Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.02255)\n- Invertible Conditional GANs for image editing [[arXiv]](https://arxiv.org/abs/1611.06355) [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_8.pdf)\n- Joint Discriminative and Generative Learning for Person Re-identification [[Project]](http://zdzheng.xyz/DG-Net/) [[Paper]](https://arxiv.org/abs/1904.07223) [[YouTube]](https://www.youtube.com/watch?v=ubCrEAIpQs4) [[Bilibili]](https://www.bilibili.com/video/av51439240) [[Poster]](http://zdzheng.xyz/images/DGNet_poster.pdf) [[Code]](https://github.com/NVlabs/DG-Net)\n- Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images [[arXiv]](https://arxiv.org/abs/1709.01993)\n- Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models [[arXiv]](https://openreview.net/pdf?id=Sy8XvGb0-)\n- Learning a Driving Simulator [[arXiv]](https://arxiv.org/abs/1608.01230)\n- Learning a Generative Adversarial Network for High Resolution Artwork Synthesis [[arXiv]](https://arxiv.org/abs/1708.09533)\n- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling [[arXiv]](https://arxiv.org/abs/1610.07584)\n- Learning Compositional Visual Concepts with Mutual Consistency [[arXiv]](https://arxiv.org/abs/1711.06148)\n- Learning from Simulated and Unsupervised Images through Adversarial Training [[arXiv]](https://arxiv.org/abs/1612.07828)\n- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05192)\n- Learning to Generate Chairs with Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1705.10413)\n- Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts [[arXiv]](https://arxiv.org/abs/1612.00215)\n- Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.07592)\n- Lessons Learned from the Training of GANs on Artificial Datasets [[arXiv]](https://arxiv.org/abs/2007.06418)\n- MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification [[arXiv]](https://arxiv.org/abs/1612.08879)\n- Megapixel Size Image Creation using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.00082)\n- Microscopy Cell Segmentation via Adversarial Neural Networks [[arXiv]](https://arxiv.org/abs/1709.05860)\n- MoCoGAN: Decomposing Motion and Content for Video Generation [[arXiv]](https://arxiv.org/abs/1707.04993)\n- Multi-view Generative Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_13.pdf)\n- Neural Photo Editing with Introspective Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_15.pdf) [[arXiv]](https://arxiv.org/abs/1609.07093)\n- Neural Stain-Style Transfer Learning using GAN for Histopathological Images [[arXiv]](https://arxiv.org/abs/1710.08543)\n- Outline Colorization through Tandem Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.08834)\n- Perceptual Adversarial Networks for Image-to-Image Transformation [[arXiv]](https://arxiv.org/abs/1706.09138)\n- Perceptual Generative Adversarial Networks for Small Object Detection [[arXiv]](https://arxiv.org/abs/1706.05274)\n- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1609.04802)\n- Pose Guided Person Image Generation [[arXiv]](https://arxiv.org/abs/1705.09368)\n- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1604.04382)\n- Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network [[arXiv]](https://arxiv.org/abs/2006.12906)\n- Recurrent Topic-Transition GAN for Visual Paragraph Generation [[arXiv]](https://arxiv.org/abs/1703.07022)\n- RenderGAN: Generating Realistic Labeled Data [[arXiv]](https://arxiv.org/abs/1611.01331)\n- Representation Learning and Adversarial Generation of 3D Point Clouds [[arXiv]](https://arxiv.org/abs/1707.02392)\n- Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs [[arXiv]](https://arxiv.org/abs/2006.16644)\n- Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution [[arXiv]](https://arxiv.org/abs/1710.04783)\n- Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.09318)\n- Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks [[arXiv]](https://arxiv.org/pdf/2008.04021.pdf)\n- SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08788)\n- SalGAN: Visual Saliency Prediction with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.01081v2)\n- SeGAN: Segmenting and Generating the Invisible [[arXiv]](https://arxiv.org/abs/1703.10239)\n- Semantic Image Inpainting with Deep Generative Models [[arXiv]](https://arxiv.org/abs/1607.07539)\n- StainGAN: Stain Style Transfer for Digital Histological Images [[arXiv]](https://arxiv.org/abs/1804.01601)\n- StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [[arXiv]](https://arxiv.org/abs/1711.09020)\n- StarGAN v2: Diverse Image Synthesis for Multiple Domains [[arXiv]](https://arxiv.org/abs/1912.01865)[[Code]](https://github.com/clovaai/stargan-v2)\n- Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs [[arXiv]](https://arxiv.org/abs/1712.02765)\n- Semantic Image Synthesis via Adversarial Learning [[arXiv]](https://arxiv.org/abs/1707.06873)\n- Semantic Segmentation using Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08408)\n- Semantically Decomposing the Latent Spaces of Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1705.07904)\n- Semi-Latent GAN: Learning to generate and modify facial images from attributes [[arXiv]](https://arxiv.org/abs/1704.02166)\n- Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.06430)\n- Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks  [[arXiv]](https://arxiv.org/abs/1711.06375)\n- Sharpness-aware Low dose CT denoising using conditional generative adversarial network [[arXiv]](https://arxiv.org/abs/1708.06453)\n- Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1708.09105)\n- SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning [[arXiv]](https://arxiv.org/abs/1810.04991) [[Code]](https://github.com/Xiaoming-Yu/SingleGAN)\n- Socially-compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning [[arXiv]](https://arxiv.org/abs/1710.02543)\n- StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.03242)\n- StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1710.10916)\n- Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN [[arXiv]](https://arxiv.org/abs/1706.03319)\n- Supervised Adversarial Networks for Image Saliency Detection [[arXiv]](https://arxiv.org/abs/1704.07242)\n- Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) [[arXiv]](https://arxiv.org/abs/1707.09747)\n- Synthesizing Filamentary Structured Images with GANs [[arXiv]](https://arxiv.org/abs/1706.02185)\n- Synthetic Iris Presentation Attack using iDCGAN [[arXiv]](https://arxiv.org/abs/1710.10565)\n- T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks [[arXiv]](https://arxiv.org/abs/1808.01454)\n- TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1703.06412)\n- Taming GANs with Lookahead [[arXiv]](https://arxiv.org/abs/2006.14567)\n- Tangent-Normal Adversarial Regularization for Semi-supervised Learning [[arXiv]](https://arxiv.org/pdf/1808.06088.pdf)\n- Temporal Generative Adversarial Nets with Singular Value Clipping [[arXiv]](https://arxiv.org/abs/1611.06624)\n- TextureGAN: Controlling Deep Image Synthesis with Texture Patches [[arXiv]](https://arxiv.org/abs/1706.02823)\n- Texture Synthesis with Spatial Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08207v3) [[Code]](https://github.com/ubergmann/spatial_gan)\n- Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language [[arXiv]](https://arxiv.org/abs/1810.11919) [[Code]](https://github.com/woozzu/tagan)\n- The Conditional Analogy GAN: Swapping Fashion Articles on People Images [[arXiv]](https://arxiv.org/abs/1709.04695)\n- Towards Adversarial Retinal Image Synthesis [[arXiv]](https://arxiv.org/abs/1701.08974) [[Code]](https://github.com/costapt/vess2ret) [[Demo]](http://vess2ret.inesctec.pt/retina)\n- Toward Multimodal Image-to-Image Translation [[arXiv]](https://arxiv.org/abs/1711.11586)\n- Towards Diverse and Natural Image Descriptions via a Conditional GAN [[arXiv]](https://arxiv.org/abs/1703.06029)\n- Towards the Automatic Anime Characters Creation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.05509)\n- TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1808.07582)\n- TripletGAN: Training Generative Model with Triplet Loss  [[arXiv]](https://arxiv.org/abs/1711.05084)\n- TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification  [[arXiv]](https://arxiv.org/abs/2007.14852)\n- TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios [[arXiv]](https://arxiv.org/pdf/2210.06609.pdf)\n- TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition  [[arXiv]](https://arxiv.org/abs/1712.02514)\n- Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs  [[arXiv]](https://arxiv.org/abs/1809.00946)\n- UGAN: Enhancing Underwater Imagery using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1801.04011)\n- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro [[arXiv]](https://arxiv.org/abs/1701.07717)[[Code]](https://github.com/layumi/Person-reID_GAN)\n- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.10593)\n- Unrolled Generative Adversarial Networks [[arXiv]](https://openreview.net/pdf?id=BydrOIcle)\n- Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification [[arXiv]](https://arxiv.org/abs/2007.15560)\n- Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1809.00437)\n- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1511.06390)\n- Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery [[arXiv]](https://arxiv.org/abs/1703.05921)\n- Unsupervised Cross-Domain Image Generation [[arXiv]](https://arxiv.org/abs/1611.02200)\n- Unsupervised Generative Adversarial Cross-modal Hashing  [[arXiv]](https://arxiv.org/abs/1712.00358)\n- Unsupervised Diverse Colorization via Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.06674)\n- Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.05424)\n- Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.09798)\n- VIGAN: Missing View Imputation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.06724)\n- Video Prediction with Appearance and Motion Conditions [[arXiv]](https://arxiv.org/abs/1807.02635)\n- WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images [[arXiv]](https://arxiv.org/abs/1702.07392)\n- Weakly Supervised Generative Adversarial Networks for 3D Reconstruction [[arXiv]](https://arxiv.org/abs/1705.10904)\n- ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions [[arXiv]](https://arxiv.org/abs/2007.12212)\n\n## Image Fusion\n- DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis [[arXiv]](https://arxiv.org/abs/2008.05865)\n- FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing [[arXiv]](https://arxiv.org/abs/2001.06968)\n- FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides [[arXiv]](https://arxiv.org/abs/2001.00692)\n- FusionGAN: A generative adversarial network for infrared and visible image fusion [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1566253518301143)\n- MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks [[paper]](https://ieeexplore.ieee.org/document/9112609)\n- MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion [[arXiv]](https://arxiv.org/abs/2009.09718)\n- Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators [[paper]](https://www.ijcai.org/Proceedings/2019/0549.pdf)\n- Spatial Fusion GAN for Image Synthesis [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhan_Spatial_Fusion_GAN_for_Image_Synthesis_CVPR_2019_paper.pdf)\n\n## Image Completion\n- Deep Portrait Image Completion and Extrapolation [[arXiv]](https://arxiv.org/pdf/1808.07757.pdf)\n- EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning [[arXiv]](https://arxiv.org/pdf/1901.00212v3.pdf)\n- Pluralistic Image Completion [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Pluralistic_Image_Completion_CVPR_2019_paper.pdf)\n- Semantic Image Completion and Enhancement using Deep Learning [[arXiv]](https://arxiv.org/pdf/1911.02222.pdf)\n\n## Medical Images\n- Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection [[arXiv]](https://arxiv.org/abs/1905.13456)\n- CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement [[arXiv]](https://arxiv.org/abs/1807.07144)\n- Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks [[paper]](https://www.nature.com/articles/s41598-019-52737-x)\n- DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images [[paper]](https://www.sciencedirect.com/science/article/pii/S1746809419302137)\n- f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518302640) [[Code]](https://github.com/tSchlegl/f-AnoGAN)\n- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification [[arXiv]](https://arxiv.org/abs/1803.01229)\n- GANs for Biological Image Synthesis [[arXiv]](https://arxiv.org/abs/1708.04692)\n- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[arXiv]](https://arxiv.org/abs/1708.00961)\n- Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images [[arXiv]](https://arxiv.org/abs/1902.09856)\n- Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [[arXiv]](https://arxiv.org/abs/2006.14761)\n- MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction  [[arXiv]](https://arxiv.org/abs/2007.13559)\n- MedGAN: Medical Image Translation using GANs [[arXiv]](https://arxiv.org/abs/1806.06397)\n- Melanoma Detection using Adversarial Training and Deep Transfer Learning [[arXiv]](https://arxiv.org/pdf/2004.06824.pdf) [[Code]](https://github.com/hasibzunair/adversarial-lesions)\n- Medical Image Generation using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/2005.10687)\n- PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation [[arXiv]](https://arxiv.org/pdf/1812.07907v1.pdf) [[Code]](https://github.com/carrenD/Medical-Cross-Modality-Domain-Adaptation)\n- Generative Adversarial Networks for Image-To-Image Translation on Multi-Contrast MR Images - A Comparision of CycleGAN and Unit [[arXiv]](https://arxiv.org/pdf/1806.07777v1.pdf) [[Code]](https://github.com/simontomaskarlsson/GAN-MRI)\n- Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs [[arXiv]](https://arxiv.org/abs/1706.02633)\n- Skin Lesion Synthesis with Generative Adversarial Networks [[arXiv]](https://arxiv.org/pdf/1902.03253v1.pdf) [[Code]](https://github.com/alceubissoto/gan-skin-lesion)\n- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[arXiv]](https://arxiv.org/abs/1706.01805)\n- Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images [[arXiv]](https://arxiv.org/abs/2006.15255)\n- Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection [[arXiv]](https://arxiv.org/abs/1906.04962)\n- Synthetic Medical Images from Dual Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.01872)\n- TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks [[scholar]](https://scholar.google.ca/scholar?hl=en\u0026as_sdt=0%2C5\u0026q=TomoGAN%3A+Low-Dose+X-Ray+Tomography+with+Generative+Adversarial+Networks\u0026btnG=) [[arXiv]](https://arxiv.org/abs/1902.07582)\n- Unpaired image denoising using a generative adversarial network in X-ray CT [[arXiv]](https://arxiv.org/abs/1903.06257)\n- XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms [[arXiv]](https://arxiv.org/abs/2007.13408)\n\n\n## Applied Other\n- Adversarial Generation of Natural Language [[arXiv]](https://arxiv.org/abs/1705.10929)\n- Adversarial Ranking for Language Generation [[arXiv]](https://arxiv.org/abs/1705.11001)\n- Adversarial Training Methods for Semi-Supervised Text Classification [[arXiv]](https://arxiv.org/abs/1605.07725) [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_12.pdf)\n- Analysis of Nonautonomous Adversarial Systems [[arXiv]](https://arxiv.org/abs/1803.05045)\n- A Generative Model for Volume Rendering [[arXiv]](A Generative Model for Volume Rendering)\n- ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? [[arXiv]](https://arxiv.org/abs/1708.08227)\n- Coverless Information Hiding Based on Generative adversarial networks   [[arXiv]](https://arxiv.org/abs/1712.06951)\n- Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models [[arXiv]](https://arxiv.org/abs/1805.06605)\n- DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data [[arXiv]](https://arxiv.org/abs/1706.02071)\n- Easy High-Dimensional Likelihood-Free Inference [[arXiv]](https://arxiv.org/abs/1711.11139)\n- Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN [[arXiv]](https://arxiv.org/abs/1702.05983)\n- Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.06490)\n- Interpolating GANs to Scaffold Autotelic Creativity [[arXiv]](https://arxiv.org/abs/2007.11119)\n- Language Generation with Recurrent Generative Adversarial Networks without Pre-training [[arXiv]](https://arxiv.org/abs/1706.01399)\n- Learning to Protect Communications with Adversarial Neural Cryptography [[arXiv]](https://arxiv.org/abs/1610.06918) [[Blog]](https://blog.acolyer.org/2017/02/10/learning-to-protect-communications-with-adversarial-neural-cryptography/)\n- Long Text Generation via Adversarial Training with Leaked Information [[arXiv]](https://arxiv.org/abs/1709.08624)\n- MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions [[arXiv]](https://arxiv.org/abs/1703.10847)\n- MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.06298)\n- One-Sided Unsupervised Domain Mapping [[arXiv]](https://arxiv.org/abs/1706.00826)\n- On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems [[arXiv]](https://arxiv.org/abs/2007.14974)\n- Reconstruction of three-dimensional porous media using generative adversarial neural networks [[arXiv]](https://arxiv.org/abs/1704.03225) [[Code]](https://github.com/LukasMosser/PorousMediaGan)\n- RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model [[arXiv]](https://arxiv.org/abs/2007.07452)\n- SVSGAN: Singing Voice Separation via Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1710.11428)\n- StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks [[arXiv]](https://arxiv.org/abs/1806.02169)\n- SEGAN: Speech Enhancement Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1703.09452)\n- Semi-supervised Learning of Compact Document Representations with Deep Networks [[Paper]](http://www.cs.nyu.edu/~ranzato/publications/ranzato-icml08.pdf)\n- SSGAN: Secure Steganography Based on Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.01613)\n- Steganographic Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05502)\n- Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization [[arXiv]](https://arxiv.org/abs/1711.02666)\n- To Create What You Tell: Generating Videos from Captions [[arXiv]](https://arxiv.org/abs/1804.08264)\n- Towards Grounding Conceptual Spaces in Neural Representations [[arXiv]](https://arxiv.org/abs/1706.04825)\n- Unsupervised Cipher Cracking Using Discrete GANs [[arXiv]](https://arxiv.org/abs/1801.04883)\n- ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network [[arXiv]](https://arxiv.org/abs/1711.02413)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpshams55%2FGAN-Case-Study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpshams55%2FGAN-Case-Study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpshams55%2FGAN-Case-Study/lists"}