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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Thomas Unterthiner, Bernhard Nessler, Calvin Seward +4
Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that…
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner +2
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training h…
Self-Normalizing Neural Networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr +1
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of D…