This repository contains the boilerplate code necessary to train Generative Adversarial Networks in the standard, non-saturating, framework and in the wasserstein framework. Under the 'impl' folder, examples can be found on how to use this framework.

-- References --

Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron C. Courville and Yoshua Bengio (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada (pp. 2672–2680).

Alec Radford and Luke Metz and Soumith Chintala (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.

Martin Arjovsky and Soumith Chintala and Léon Bottou (2017). Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (pp. 214–223). PMLR.

Ishaan Gulrajani and Faruk Ahmed and Martin Arjovsky and Vincent Dumoulin and Aaron C. Courville (2017). Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA (pp. 5767–5777).