- [Generative Adversarial Networks (GANs)]
- [Variational Autoencoders (VAEs)]
- [Autoregressive models]
- [EX: PixelRNN]
(I have listed below GANs based on their timelines, since from GANs inception(Jun 2014))
- [DCGANs]
- [cGANs]
- [InfoGANs]
- [Wasserstein GANs]
- [LAPGAN]
- [GRAN ]
- [BiGANs]
- [f-GAN]
- [CoGAN]
- [EBGAN]
- [iGAN]
- [SeqGAN]
- [RenderGAN]
- [VGAN]
- [LSGANs]
- [IcGAN]
- [TGAN]
- [SAD-GAN]
- [C-RNN-GAN]
- [AANs]
- [StackGAN]
- [SGAN]
- [SalGAN]
- [AdaGAN]
- [GLS-GAN]
- [ArtGAN]
- [BS-GAN]
- [AM-GAN]
- [Triple-GAN]
- [DiscoGAN]
- [SEGAN]
- [CVAE-GAN]
- [SeGAN]
- [CycleGAN]
- [BEGAN]
- [MidiNet]
- [Semi-Latent GAN]
- [DualGAN]
- [A-Fast-RCNN]
- [MAGAN]
- [Gang of GANs]
- [Softmax GAN]
- [Show, Adapt and Tell]
- [Geometric GAN]
- [GeneGAN]
- [Flow-GAN]
- [SegAN]
- [DeLiGAN]
- [StackGAN]
- [Flow-GAN]
- [GraphGAN]
- [MuseGAN]
- [OptionGAN]
- [RAN4IQA]
- [Show, Reward and Tell]
More GANs --> The GAN Zoo
- Generative Adversarial Networks: An Overview [arXiv]
- NIPS 2016 Tutorial: Generative Adversarial Networks
- Survey on Generative Adversarial Networks
- Comparative Study on Generative Adversarial Networks
- GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
- A Survey of Image Synthesis and Editing with Generative Adversarial Networks
- OpenAI: Generative Models
- Generative models
- Adversarial Examples: Attacks and Defenses for Deep Learning
- Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
- The GAN Landscape: Losses, Architectures, Regularization, and Normalization
- A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models
- QGAN: Quantized Generative Adversarial Networks
Maintainer
Gopala KR / @gopala-kr