Comparing GANS with three different metrics for a Discrete Optimization and Machine Learning mathematics Seminar @TU Berlin The code is based upon the following papers and implementations:
Ian J. Goodfellow et. al.: Generative Adversarial Nets, https://arxiv.org/pdf/1406.2661.pdf Radford et. al.: UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS, https://arxiv.org/pdf/1511.06434.pdf Martin Arjovsky et. al.: Wasserstein GAN, https://arxiv.org/pdf/1701.07875.pdf
Alladin Pearson: Machine Learning Collection https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML (Online Tutorial for Coding GANs) https://github.com/Newmu/dcgan_code: Source Code of the DCGAN Paper https://github.com/pytorch/examples/tree/master/dcgan: Pytorch Guide, GAN Example