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 …

With the advent of generative adversarial networks, synthesizing images from text
descriptions has recently become an active research area. It is a flexible and intuitive way for …

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven
unconditional generative image modeling. We expose and analyze several of its …

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

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent
advances in generative visual models and neural rendering. Existing approaches however …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

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 …

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

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 …

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 …