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arXiv · 2012.00732

Convergence and Sample Complexity of SGD in GANs

Vasilis Kontonis·Sihan Liu·Christos Tzamos
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Abstract

We provide theoretical convergence guarantees on training Generative Adversarial Networks (GANs) via SGD. We consider learning a target distribution modeled by a 1-layer Generator network with a non-linear activation function $ϕ(\cdot)$ parametrized by a $d \times d$ weight matrix $\mathbf W_*$, i.e., $f_*(\mathbf x) = ϕ(\mathbf W_* \mathbf x)$. Our main result is that by training the Generator together with a Discriminator according to the Stochastic Gradient Descent-Ascent iteration proposed by Goodfellow et al. yields a Generator distribution that approaches the target distribution of $f_*$. Specifically, we can learn the target distribution within total-variation distance $ε$ using $\tilde O(d^2/ε^2)$ samples which is (near-)information theoretically optimal. Our results apply to a broad class of non-linear activation functions $ϕ$, including ReLUs and is enabled by a connection with truncated statistics and an appropriate design of the Discriminator network. Our approach relies on a bilevel optimization framework to show that vanilla SGDA works.

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Vasilis Kontonis, Sihan Liu, Christos Tzamos. 2020-12-01. Convergence and Sample Complexity of SGD in GANs. https://arxiv.org/abs/2012.00732

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