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 …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

Score-based generative modeling (SGM) has grown to be a hugely successful method for
learning to generate samples from complex data distributions such as that of images and …

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 (GAN) are a powerful subclass of generative models.
Despite a very rich research activity leading to numerous interesting GAN algorithms, it is …

Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

Measuring mutual information from finite data is difficult. Recent work has considered
variational methods maximizing a lower bound. In this paper, we prove that serious …

In this paper, we show that Generative Adversarial Networks (GANs) suffer from catastrophic
forgetting even when they are trained to approximate a single target distribution. We show …

A longstanding problem in machine learning is to find unsupervised methods that can learn
the statistical structure of high dimensional signals. In recent years, GANs have gained much …