Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

In standard generative adversarial network (SGAN), the discriminator estimates the
probability that the input data is real. The generator is trained to increase the probability that …

Generative models, in particular generative adversarial networks (GANs), have gained
significant attention in recent years. A number of GAN variants have been proposed and …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

Cross-domain alignment between two sets of entities (eg, objects in an image, words in a
sentence) is fundamental to both computer vision and natural language processing. Existing …

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable
users to check billions of passwords per second against password hashes. In addition to …

Generative Adversarial Networks (GAN) can achieve promising performance on learning
complex data distributions on different types of data. In this paper, we first show a …

Generative adversarial networks (GANs) represent a zero-sum game between two machine
players, a generator and a discriminator, designed to learn the distribution of data. While …

Generative Adversarial Networks (GANs) are one of the most popular tools for learning
complex high dimensional distributions. However, generalization properties of GANs have …