Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

We consider the problem of detecting out-of-distribution images in neural networks. We
propose ODIN, a simple and effective method that does not require any change to a pre …

Devising domain-and model-agnostic evaluation metrics for generative models is an
important and as yet unresolved problem. Most existing metrics, which were tailored solely …

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In
this new model, we show that we can improve the stability of learning, get rid of problems …

Generative Adversarial Networks (GANs) have shown remarkable success as a framework
for training models to produce realistic-looking data. In this work, we propose a Recurrent …

Generative moment matching network (GMMN) is a deep generative model that differs from
Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two …

The learning of domain-invariant representations in the context of domain adaptation with
neural networks is considered. We propose a new regularization method that minimizes the …

Generative adversarial networks (GANs) are a technique for learning generative models of
complex data distributions from samples. Despite remarkable advances in generating …

We study the problem of learning conditional generators from noisy labeled samples, where
the labels are corrupted by random noise. A standard training of conditional GANs will not …