- Generative Adversarial Networks: An Overview, 2017
- Generative Adversarial Networks: Introduction and Outlook, 2017
- The first paper specifically on GANs as a generative model was published by Ian Goodfellow, et al. in 2014 titled “Generative Adversarial Networks.”
- General technique with simple examples of generating images from MNIST (handwritten digits), CIFAR-10 (small photographs), and faces
- Alec Radford, et al - “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks”
- DCGAN : Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
- Improved Techniques for Training GANs, 2016.
- Energy-based Generative Adversarial Network, 2016.
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, 2016
- Wasserstein GAN, 2017
- Are GANs Created Equal? A Large-Scale Study, 2017
- The GAN Landscape: Losses, Architectures, Regularization, and Normalization, 2018
- Generative Adversarial Networks, Ian Goodfellow, NIPS 2016 Tutorial
- NIPS 2016 Tutorial: Generative Adversarial Networks, Slides, 2016
- NIPS 2016 Tutorial: Generative Adversarial Networks, Paper, 2016.
- Generative Adversarial Networks, Ian Goodfellow, AIWTB, 2016
- Adversarial Machine Learning, Ian Goodfellow, AAAI, 2019
- a more academic presentation on GANs - lecture on Generative Models from the Stanford course on Convolutional Neural Networks