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arXiv · 1810.01365

On Self Modulation for Generative Adversarial Networks

Ting Chen·Mario Lucic·Neil Houlsby·Sylvain Gelly
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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.

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Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly. 2019-05-02. On Self Modulation for Generative Adversarial Networks. https://arxiv.org/abs/1810.01365

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