1,226 research outputs found
Max-Sliced Wasserstein Distance and its use for GANs
Generative adversarial nets (GANs) and variational auto-encoders have
significantly improved our distribution modeling capabilities, showing promise
for dataset augmentation, image-to-image translation and feature learning.
However, to model high-dimensional distributions, sequential training and
stacked architectures are common, increasing the number of tunable
hyper-parameters as well as the training time. Nonetheless, the sample
complexity of the distance metrics remains one of the factors affecting GAN
training. We first show that the recently proposed sliced Wasserstein distance
has compelling sample complexity properties when compared to the Wasserstein
distance. To further improve the sliced Wasserstein distance we then analyze
its `projection complexity' and develop the max-sliced Wasserstein distance
which enjoys compelling sample complexity while reducing projection complexity,
albeit necessitating a max estimation. We finally illustrate that the proposed
distance trains GANs on high-dimensional images up to a resolution of 256x256
easily.Comment: Accepted to CVPR 201
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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