Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

This work is an update of my previous paper on the same topic published a few years ago
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …

Cutting-edge diffusion models produce images with high quality and customizability,
enabling them to be used for commercial art and graphic design purposes. But do diffusion …

In the space of only a few years, deep generative modeling has revolutionized how we think
of artificial creativity, yielding autonomous systems which produce original images, music …

Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
However, the success of the GAN models hinges on a large amount of training data. This …

Many generative models synthesize data by transforming a standard Gaussian random
variable using a deterministic neural network. Among these models are the Variational …

Data is the foundation of most science. Unfortunately, sharing data can be obstructed by the
risk of violating data privacy, impeding research in fields like healthcare. Synthetic data is a …

Abstract Generative Adversarial Networks (GANs) rely heavily on large-scale training data
for training high-quality image generation models. With limited training data, the GAN …

A learned generative model often produces biased statistics relative to the underlying data
distribution. A standard technique to correct this bias is importance sampling, where …

We develop a novel generative model for zero-shot learning to recognize fine-grained
unseen classes without training samples. Our observation is that generating holistic features …