On Self Modulation for Generative Adversarial Networks

ICLR 2019 Ting ChenMario LucicNeil HoulsbySylvain Gelly

Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings... (read more)

PDF Abstract

Code


↳ Quickstart in
1,604
↳ Quickstart in
0

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper