On Self Modulation for Generative Adversarial Networks


Sep 27, 2018 (modified: Nov 12, 2018) ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: 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. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of 5%-35% in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in 124/144 (86%) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN.
  • Keywords: unsupervised learning, generative adversarial networks, deep generative modelling
  • TL;DR: A simple GAN modification that improves performance across many losses, architectures, regularization schemes, and datasets.
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