GitHub repository: JulianTrapp/Wasserstein-Generative-Adversarial-Network
Created: 2020-10-04T16:02:19Z
Description: In this Machine Learning Exam I created two types of Generative Adversarial Networks (GANs) with different loss functions. The aim of the two networks is to train the generator network to generate samples from a given probability distribution in a way that the discriminator cannot distinguish it from real samples. In this example I worked with data from a 2D Ising Model which is one of the most successful and broadly applicable models in statistical physics.