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 …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Abstract Domain generalization aims to learn from multiple source domains a predictive
model that can generalize to unseen target domains. One essential problem in domain …

Abstract Conditional Generative Adversarial Networks (cGAN) generate realistic images by
incorporating class information into GAN. While one of the most popular cGANs is an …

Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for
realistic image synthesis. While training and evaluating GAN becomes increasingly …

Unsupervised domain adaptation, as a prevalent transfer learning setting, spans many real-
world applications. With the increasing representational power and applicability of neural …

Conditional generative models aim to learn the underlying joint distribution of data and
labels to achieve conditional data generation. Among them, the auxiliary classifier …

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

This paper is concerned with data-driven unsupervised domain adaptation, where it is
unknown in advance how the joint distribution changes across domains, ie, what factors or …

Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of
translation functions can map the source domain distribution to the target distribution …