the-gan-zoo

# The GAN Zoo

Every week, new GAN papers are coming out and it's hard to keep track of them all, not to mention the incredibly creative ways in which researchers are naming these GANs! So, here's a list of what started as a fun activity compiling all named GANs!

You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title [here](https://github.com/hindupuravinash/the-gan-zoo/blob/master/gans.tsv). Contributions are welcome. Add links through pull requests in gans.tsv file in the same format or create an issue to lemme know something I missed or to start a discussion. Check out [Deep Hunt](https://deephunt.in) - my weekly AI newsletter for this repo as [blogpost](https://medium.com/deep-hunt/the-gan-zoo-79597dc8c347) and follow me on [Twitter](https://www.twitter.com/hindupuravinash). * 3D-ED-GAN - [Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks](https://arxiv.org/abs/1711.06375) * 3D-GAN - [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://arxiv.org/abs/1610.07584) ([github](https://github.com/zck119/3dgan-release)) * 3D-IWGAN - [Improved Adversarial Systems for 3D Object Generation and Reconstruction](https://arxiv.org/abs/1707.09557) ([github](https://github.com/EdwardSmith1884/3D-IWGAN)) * 3D-PhysNet - [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https://arxiv.org/abs/1805.00328) * 3D-RecGAN - [3D Object Reconstruction from a Single Depth View with Adversarial Learning](https://arxiv.org/abs/1708.07969) ([github](https://github.com/Yang7879/3D-RecGAN)) * ABC-GAN - [ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks](https://drive.google.com/file/d/0B3wEP_lEl0laVTdGcHE2VnRiMlE/view) ([github](https://github.com/IgorSusmelj/ABC-GAN)) * ABC-GAN - [GANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference](https://arxiv.org/abs/1711.11139) * AC-GAN - [Conditional Image Synthesis With Auxiliary Classifier GANs](https://arxiv.org/abs/1610.09585) * acGAN - [Face Aging With Conditional Generative Adversarial Networks](https://arxiv.org/abs/1702.01983) * ACGAN - [Coverless Information Hiding Based on Generative adversarial networks](https://arxiv.org/abs/1712.06951) * acGAN - [On-line Adaptative Curriculum Learning for GANs](https://arxiv.org/abs/1808.00020) * ACtuAL - [ACtuAL: Actor-Critic Under Adversarial Learning](https://arxiv.org/abs/1711.04755) * AdaGAN - [AdaGAN: Boosting Generative Models](https://arxiv.org/abs/1701.02386v1) * Adaptive GAN - [Customizing an Adversarial Example Generator with Class-Conditional GANs](https://arxiv.org/abs/1806.10496) * AdvEntuRe - [AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https://arxiv.org/abs/1805.04680) * AdvGAN - [Generating adversarial examples with adversarial networks](https://arxiv.org/abs/1801.02610) * AE-GAN - [AE-GAN: adversarial eliminating with GAN](https://arxiv.org/abs/1707.05474) * AE-OT - [Latent Space Optimal Transport for Generative Models](https://arxiv.org/abs/1809.05964) * AEGAN - [Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets](https://arxiv.org/abs/1703.10094) * AF-DCGAN - [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System](https://arxiv.org/abs/1804.05347) * AffGAN - [Amortised MAP Inference for Image Super-resolution](https://arxiv.org/abs/1610.04490) * AIM - [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://arxiv.org/abs/1809.05972) * AL-CGAN - [Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts](https://arxiv.org/abs/1612.00215) * ALI - [Adversarially Learned Inference](https://arxiv.org/abs/1606.00704) ([github](https://github.com/IshmaelBelghazi/ALI)) * AlignGAN - [AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks](https://arxiv.org/abs/1707.01400) * AlphaGAN - [AlphaGAN: Generative adversarial networks for natural image matting](https://arxiv.org/abs/1807.10088) * AM-GAN - [Activation Maximization Generative Adversarial Nets](https://arxiv.org/abs/1703.02000) * AmbientGAN - [AmbientGAN: Generative models from lossy measurements](https://openreview.net/forum?id=Hy7fDog0b) ([github](https://github.com/AshishBora/ambient-gan)) * AMC-GAN - [Video Prediction with Appearance and Motion Conditions](https://arxiv.org/abs/1807.02635) * AnoGAN - [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://arxiv.org/abs/1703.05921v1) * APD - [Adversarial Distillation of Bayesian Neural Network Posteriors](https://arxiv.org/abs/1806.10317) * APE-GAN - [APE-GAN: Adversarial Perturbation Elimination with GAN](https://arxiv.org/abs/1707.05474) * ARAE - [Adversarially Regularized Autoencoders for Generating Discrete Structures](https://arxiv.org/abs/1706.04223) ([github](https://github.com/jakezhaojb/ARAE)) * ARDA - [Adversarial Representation Learning for Domain Adaptation](https://arxiv.org/abs/1707.01217) * ARIGAN - [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https://arxiv.org/abs/1709.00938) * ArtGAN - [ArtGAN: Artwork Synthesis with Conditional Categorial GANs](https://arxiv.org/abs/1702.03410) * ASDL-GAN - [Automatic Steganographic Distortion Learning Using a Generative Adversarial Network](https://ieeexplore.ieee.org/document/8017430/) * ATA-GAN - [Attention-Aware Generative Adversarial Networks (ATA-GANs)](https://arxiv.org/abs/1802.09070) * Attention-GAN - [Attention-GAN for Object Transfiguration in Wild Images](https://arxiv.org/abs/1803.06798) * AttGAN - [Arbitrary Facial Attribute Editing: Only Change What You Want](https://arxiv.org/abs/1711.10678) ([github](https://github.com/LynnHo/AttGAN-Tensorflow)) * AttnGAN - [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://arxiv.org/abs/1711.10485) ([github](https://github.com/taoxugit/AttnGAN)) * AVID - [AVID: Adversarial Visual Irregularity Detection](https://arxiv.org/abs/1805.09521) * B-DCGAN - [B-DCGAN:Evaluation of Binarized DCGAN for FPGA](https://arxiv.org/abs/1803.10930) * b-GAN - [Generative Adversarial Nets from a Density Ratio Estimation Perspective](https://arxiv.org/abs/1610.02920) * BAGAN - [BAGAN: Data Augmentation with Balancing GAN](https://arxiv.org/abs/1803.09655) * Bayesian GAN - [Deep and Hierarchical Implicit Models](https://arxiv.org/abs/1702.08896) * Bayesian GAN - [Bayesian GAN](https://arxiv.org/abs/1705.09558) ([github](https://github.com/andrewgordonwilson/bayesgan/)) * BCGAN - [Bayesian Conditional Generative Adverserial Networks](https://arxiv.org/abs/1706.05477) * BCGAN - [Bidirectional Conditional Generative Adversarial networks](https://arxiv.org/abs/1711.07461) * BEAM - [Boltzmann Encoded Adversarial Machines](https://arxiv.org/abs/1804.08682) * BEGAN - [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/abs/1703.10717) * BEGAN-CS - [Escaping from Collapsing Modes in a Constrained Space](https://arxiv.org/abs/1808.07258) * Bellman GAN - [Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN](https://arxiv.org/abs/1808.01960) * BGAN - [Binary Generative Adversarial Networks for Image Retrieval](https://arxiv.org/abs/1708.04150) ([github](https://github.com/htconquer/BGAN)) * Bi-GAN - [Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm](https://arxiv.org/abs/1809.10244) * BicycleGAN - [Toward Multimodal Image-to-Image Translation](https://arxiv.org/abs/1711.11586) ([github](https://github.com/junyanz/BicycleGAN)) * BiGAN - [Adversarial Feature Learning](https://arxiv.org/abs/1605.09782v7) * BinGAN - [BinGAN: Learning Compact Binary Descriptors with a Regularized GAN](https://arxiv.org/abs/1806.06778) * BourGAN - [BourGAN: Generative Networks with Metric Embeddings](https://arxiv.org/abs/1805.07674) * BranchGAN - [Branched Generative Adversarial Networks for Multi-Scale Image Manifold Learning](https://arxiv.org/abs/1803.08467) * BRE - [Improving GAN Training via Binarized Representation Entropy (BRE) Regularization](https://arxiv.org/abs/1805.03644) ([github](https://github.com/BorealisAI/bre-gan)) * BridgeGAN - [Generative Adversarial Frontal View to Bird View Synthesis](https://arxiv.org/abs/1808.00327) * BS-GAN - [Boundary-Seeking Generative Adversarial Networks](https://arxiv.org/abs/1702.08431v1) * BubGAN - [BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images](https://arxiv.org/abs/1809.02266) * BWGAN - [Banach Wasserstein GAN](https://arxiv.org/abs/1806.06621) * C-GAN - [Face Aging with Contextual Generative Adversarial Nets ](https://arxiv.org/abs/1802.00237 ) * C-RNN-GAN - [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://arxiv.org/abs/1611.09904) ([github](https://github.com/olofmogren/c-rnn-gan/)) * CA-GAN - [Composition-aided Sketch-realistic Portrait Generation](https://arxiv.org/abs/1712.00899) * CaloGAN - [CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks](https://arxiv.org/abs/1705.02355) ([github](https://github.com/hep-lbdl/CaloGAN)) * CAN - [CAN: Creative Adversarial Networks, Generating Art by Learning About Styles and Deviating from Style Norms](https://arxiv.org/abs/1706.07068) * CapsGAN - [CapsGAN: Using Dynamic Routing for Generative Adversarial Networks](https://arxiv.org/abs/1806.03968) * CapsuleGAN - [CapsuleGAN: Generative Adversarial Capsule Network ](http://arxiv.org/abs/1802.06167) * CatGAN - [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https://arxiv.org/abs/1511.06390v2) * CatGAN - [CatGAN: Coupled Adversarial Transfer for Domain Generation](https://arxiv.org/abs/1711.08904) * CausalGAN - [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https://arxiv.org/abs/1709.02023) * CC-GAN - [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https://arxiv.org/abs/1611.06430) ([github](https://github.com/edenton/cc-gan)) * cd-GAN - [Conditional Image-to-Image Translation](https://arxiv.org/abs/1805.00251) * CDcGAN - [Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network](https://arxiv.org/abs/1708.09105) * CE-GAN - [Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network](https://arxiv.org/abs/1807.04585) * CFG-GAN - [Composite Functional Gradient Learning of Generative Adversarial Models](https://arxiv.org/abs/1801.06309) * CGAN - [Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784) * CGAN - [Controllable Generative Adversarial Network](https://arxiv.org/abs/1708.00598) * Chekhov GAN - [An Online Learning Approach to Generative Adversarial Networks](https://arxiv.org/abs/1706.03269) * ciGAN - [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https://arxiv.org/abs/1807.08093) * CinCGAN - [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https://arxiv.org/abs/1809.00437) * CipherGAN - [Unsupervised Cipher Cracking Using Discrete GANs](https://arxiv.org/abs/1801.04883) * ClusterGAN - [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https://arxiv.org/abs/1809.03627) * CM-GAN - [CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning](https://arxiv.org/abs/1710.05106) * CoAtt-GAN - [Are You Talking to Me? Reas