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) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Abstract Generative Adversarial Network (GAN) is a research hotspot in deep generative
models, which has been widely used in the field of medical image fusion. This paper …

Conventional unsupervised domain adaptation (UDA) assumes that training data are
sampled from a single domain. This neglects the more practical scenario where training data …

We investigate the training and performance of generative adversarial networks using the
Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical …

What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we
modify a classical GAN, ie., StyleGANv2, as little as possible. We find that only two …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

With the recent success of deep neural networks, remarkable progress has been achieved
on face recognition. However, collecting large-scale real-world training data for face …

Generative Adversarial Nets (GANs) represent an important milestone for effective
generative models, which has inspired numerous variants seemingly different from each …

We present variational generative adversarial networks, a general learning framework that
combines a variational auto-encoder with a generative adversarial network, for synthesizing …