The emergence of deep learning model GAN (Generative Adversarial Networks) is an
important turning point in generative modeling. GAN is more powerful in feature and …

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs)
have become the preferred option for generative modeling, with numerous pre-trained …

Training generative adversarial networks (GAN) using too little data typically leads to
discriminator overfitting, causing training to diverge. We propose an adaptive discriminator …

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 …

The video generation task can be formulated as a prediction of future video frames given
some past frames. Recent generative models for videos face the problem of high …

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials
design problems. This paper aims to review recent advances in AI‐driven materials‐by …

Video inpainting remains a challenging problem to fill with plausible and coherent content in
unknown areas in video frames despite the prevalence of data-driven methods. Although …

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

Generating long-range skeleton-based human actions has been a challenging problem
since small deviations of one frame can cause a malformed action sequence. Most existing …

In this paper, we improve Generative Adversarial Networks by incorporating a manifold
learning step into the discriminator. We consider locality-constrained linear and subspace …