Generative Adversarial Network, Autoencoder and Normalizing Flow

Generative Adversarial Network (GAN)

Awesome-GANs papers and codes: [kozistr/Awesome-GANs], [hollobit/All-About-the-GAN].
[2014 NIPS] Generative Adversarial Nets, [paper], [bibtex], sources: [goodfeli/adversarial], [aymericdamien/TensorFlow-Examples].
[2016 ICLR] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [paper], [bibtex] sources: [Newmu/dcgan_code].
[2017 ICLR] Towards Principled Methods for Training Generative Adversarial Networks, [paper], [bibtex].
[2017 ICML] Wasserstein Generative Adversarial Networks, [paper], [supplementary], [arxiv], [bibtex], [homepage], [explain1], [explain2], [explain3], [explain4], sources: [kpandey008/wasserstein-gans], [martinarjovsky/WassersteinGAN], [luslab/scRNAseq-WGAN-GP].
[2017 AAAI] SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient, [paper], [bibtex], sources:[LantaoYu/SeqGAN].
[2017 ICCV] CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [paper], [bibtex], [homepage], sources: [junyanz/pytorch-CycleGAN-and-pix2pix], [junyanz/CycleGAN], [xhujoy/CycleGAN-tensorflow].
[2018 ICLR] CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training, [paper], [bibtex], sources: [mkocaoglu/CausalGAN].
[2018 ICLR] Progressive Growing of GANs for Improved Quality, Stability, and Variation, [paper], [bibtex], sources: [tkarras/progressive_growing_of_gans].
[2018 ArXiv] A Style-Based Generator Architecture for Generative Adversarial Networks, [paper], [bibtex], [blog], sources: [NVlabs/stylegan], [Puzer/stylegan-encoder].
[2018 ArXiv] XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings, [paper], [bibtex], [Cartoon Dataset].
[2018 ICLR] Spectral Normalization for Generative Adversarial Networks, [paper], [bibtex], sources: [pfnet-research/sngan_projection].
[2019 ICLR] On Computation and Generalization of Generative Adversarial Networks under Spectrum Control, [paper], [bibtex], sources: [HMJiangGatech/spectral-control-GAN].

Autoencoder

[CS294A] Sparse Autoencoder, [lecture notes].
[2014 ICLR] k-Sparse Autoencoders, [paper], [bibtex], sources: [arashsaber/Sparse-Auto-Encoder], [snooky23/K-Sparse-AutoEncoder].
[2015] Adversarial Autoencoders, [paper], [slides], [bibtex], sources: [conan7882/adversarial-autoencoders], [neale/Adversarial-Autoencoder].
[2020 ICLR] From Variational to Deterministic Autoencoders, [paper], [bibtex], sources: [ParthaEth/Regularized_autoencoders-RAE-].

Normalizing Flow

[2015 ICLR] NICE: Non-linear Independent Components Estimation, [paper], [bibtex], sources: [paultsw/nice_pytorch], [DakshIdnani/pytorch-nice], [laurent-dinh/nice].
[2017 ICLR] Density Estimation using Real NVP, [paper], [bibtex], sources: [chrischute/real-nvp], [xqding/RealNVP].
[2017 NeurIPS] The Reversible Residual Network: Backpropagation Without Storing Activations, [paper], [bibtex], sources: [renmengye/revnet-public].
[2018 NeurIPS] Glow: Generative Flow with Invertible 1×1 Convolutions, [paper], [bibtex], sources: [openai/glow], [chaiyujin/glow-pytorch], [rosinality/glow-pytorch], [samuelmat19/GLOW-tf2].
[2020 TPAMI] Normalizing Flows: An Introduction and Review of Current Methods, [paper], [ArXiv v1], [bibtex].
[2021 ArXiv] GFlowNet Foundations, [paper], [bibtex].