SGD Learns One-Layer Networks in WGANs

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • TL;DR: We show that stochastic gradient descent ascent converges to a global optimum for WGAN with one-layer generator network.
  • Abstract: Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax problem to global optimality, but in practice, are successfully trained with stochastic gradient descent-ascent. In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution in polynomial time and sample complexity.
  • Code: https://colab.research.google.com/drive/1P1hBwPcq21oj2IroX1rWXUJ7NUCD33CH
  • Keywords: Wasserstein GAN, global min-max, one-layer network
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