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 …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

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 …

Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Smart manufacturing refers to using advanced data analytics to complement physical
science for improving system performance and decision making. With the widespread …

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 …

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

We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a
generative model of the data distribution. WAE minimizes a penalized form of the …