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 …

Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

We present a comprehensive solution to learn and improve text-to-image models from
human preference feedback. To begin with, we build ImageReward---the first general …

Recent text-to-image generation methods provide a simple yet exciting conversion capability
between text and image domains. While these methods have incrementally improved the …

Standard diffusion models involve an image transform--adding Gaussian noise--and an
image restoration operator that inverts this degradation. We observe that the generative …

We show that diffusion models can achieve image sample quality superior to the current
state-of-the-art generative models. We achieve this on unconditional image synthesis by …

The output of text-to-image synthesis systems should be coherent, clear, photo-realistic
scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal …

Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these
networks are trained on something new, they rapidly forget what was learned before. In the …

Score-based generative models can produce high quality image samples comparable to
GANs, without requiring adversarial optimization. However, existing training procedures are …

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large
scale image generation. To this end, we scale and enhance the autoregressive priors used …