3,087 research outputs found
Wasserstein Divergence for GANs
In many domains of computer vision, generative adversarial networks (GANs)
have achieved great success, among which the family of Wasserstein GANs (WGANs)
is considered to be state-of-the-art due to the theoretical contributions and
competitive qualitative performance. However, it is very challenging to
approximate the -Lipschitz constraint required by the Wasserstein-1
metric~(W-met). In this paper, we propose a novel Wasserstein
divergence~(W-div), which is a relaxed version of W-met and does not require
the -Lipschitz constraint. As a concrete application, we introduce a
Wasserstein divergence objective for GANs~(WGAN-div), which can faithfully
approximate W-div through optimization. Under various settings, including
progressive growing training, we demonstrate the stability of the proposed
WGAN-div owing to its theoretical and practical advantages over WGANs. Also, we
study the quantitative and visual performance of WGAN-div on standard image
synthesis benchmarks of computer vision, showing the superior performance of
WGAN-div compared to the state-of-the-art methods.Comment: accepted by eccv_2018, correct minor error
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
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.Comment: This is an updated version of an ICML'17 paper with the same title.
The main difference is that in the ICML'17 version the pure equilibrium
result was only proved for Wasserstein GAN. In the current version the result
applies to most reasonable training objectives. In particular, Theorem 4.3
now applies to both original GAN and Wasserstein GA
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