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)

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