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arXiv · 1703.00573

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

Sanjeev Arora·Rong Ge·Yingyu Liang·Tengyu Ma·Yi Zhang
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Abstract

We show that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. However, generalization does occur for a weaker metric called neural net distance. It is also shown that an approximate pure equilibrium exists in the discriminator/generator game for a special class of generators with natural training objectives when generator capacity and training set sizes are moderate. This existence of equilibrium inspires MIX+GAN protocol, which can be combined with any existing GAN training, and empirically shown to improve some of them.

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Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang. 2017-08-01. Generalization and Equilibrium in Generative Adversarial Nets (GANs). https://arxiv.org/abs/1703.00573

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