Avinash Hindupur
#1 @babygogo_in, @iitguwahati alum. Creator of @deephunt_in, Organiser @ Delhi Kaggle Group | Interested in all things data and machine learning.
The GAN Zoo
A list of all named GANs!
Every week, new papers on Generative Adversarial Networks (GAN) 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! You can read more about GANs in this Generative Models post by OpenAI or this overview tutorial in KDNuggets.
So, here’s the current and frequently updated list, from what started as a fun activity compiling all named GANs in this format: Name and Source Paper linked to Arxiv.
- GAN — Generative Adversarial Networks
- 3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
- AdaGAN — AdaGAN: Boosting Generative Models
- AffGAN — Amortised MAP Inference for Image Super-resolution
- ALI — Adversarially Learned Inference
- AMGAN — Generative Adversarial Nets with Labeled Data by Activation Maximization
- AnoGAN — Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
- ArtGAN — ArtGAN: Artwork Synthesis with Conditional Categorial GANs
- b-GAN — b-GAN: Unified Framework of Generative Adversarial Networks
- Bayesian GAN — Deep and Hierarchical Implicit Models
- BEGAN — BEGAN: Boundary Equilibrium Generative Adversarial Networks
- BiGAN — Adversarial Feature Learning
- BS-GAN — Boundary-Seeking Generative Adversarial Networks
- CGAN — Towards Diverse and Natural Image Descriptions via a Conditional GAN
- CCGAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
- CatGAN — Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
- CoGAN — Coupled Generative Adversarial Networks
- Context-RNN-GAN — Contextual RNN-GANs for Abstract Reasoning Diagram Generation
- C-RNN-GAN — C-RNN-GAN: Continuous recurrent neural networks with adversarial training
- CVAE-GAN — CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
- CycleGAN — Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- DTN — Unsupervised Cross-Domain Image Generation
- DCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- DiscoGAN — Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
- DualGAN — DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
- EBGAN — Energy-based Generative Adversarial Network
- f-GAN — f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
- GoGAN — Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
- GP-GAN — GP-GAN: Towards Realistic High-Resolution Image Blending
- IAN — Neural Photo Editing with Introspective Adversarial Networks
- iGAN — Generative Visual Manipulation on the Natural Image Manifold
- IcGAN — Invertible Conditional GANs for image editing
- ID-CGAN- Image De-raining Using a Conditional Generative Adversarial Network
- Improved GAN — Improved Techniques for Training GANs
- InfoGAN — InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
- LR-GAN — LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
- LSGAN — Least Squares Generative Adversarial Networks
- LS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
- MGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
- MAGAN — MAGAN: Margin Adaptation for Generative Adversarial Networks
- MalGAN — Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
- MARTA-GAN — Deep Unsupervised Representation Learning for Remote Sensing Images
- McGAN — McGan: Mean and Covariance Feature Matching GAN
- MedGAN — Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks
- MIX+GAN — Generalization and Equilibrium in Generative Adversarial Nets (GANs)
- MPM-GAN — Message Passing Multi-Agent GANs
- MV-BiGAN — Multi-view Generative Adversarial Networks
- pix2pix — Image-to-Image Translation with Conditional Adversarial Networks
- PPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
- PrGAN — 3D Shape Induction from 2D Views of Multiple Objects
- RenderGAN — RenderGAN: Generating Realistic Labeled Data
- RTT-GAN — Recurrent Topic-Transition GAN for Visual Paragraph Generation
- SGAN — Stacked Generative Adversarial Networks
- SGAN — Texture Synthesis with Spatial Generative Adversarial Networks
- SAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
- SalGAN — SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
- SEGAN — SEGAN: Speech Enhancement Generative Adversarial Network
- SeqGAN — SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
- SketchGAN — Adversarial Training For Sketch Retrieval
- SL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributes
- SRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- S²GAN — Generative Image Modeling using Style and Structure Adversarial Networks
- SSL-GAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
- StackGAN — StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
- TGAN — Temporal Generative Adversarial Nets
- TAC-GAN — TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial Network
- TP-GAN — Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
- Triple-GAN — Triple Generative Adversarial Nets
- VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models
- VAE-GAN — Autoencoding beyond pixels using a learned similarity metric
- ViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworks
- WGAN — Wasserstein GAN
- WGAN-GP — Improved Training of Wasserstein GANs
- WaterGAN — WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
Visit the Github repository to add more links via pull requests or create an issue to lemme know something I missed or to start a discussion.
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