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 …

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

Can a generative model be trained to produce images from a specific domain, guided only
by a text prompt, without seeing any image? In other words: can an image generator be …

Consistency models are a nascent family of generative models that can sample high quality
data in one step without the need for adversarial training. Current consistency models …

Generative Adversarial Networks (GANs) have achieved remarkable achievements in image
synthesis. These successes of GANs rely on large scale datasets, requiring too much cost …

We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In
this paper, we introduce Generative Scene Networks (GSN), which learns to decompose …

Food recognition plays an important role in food choice and intake, which is essential to the
health and well‐being of humans. It is thus of importance to the computer vision community …

The advent of large-scale training has produced a cornucopia of powerful visual recognition
models. However, generative models, such as GANs, have traditionally been trained from …

Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for
realistic image synthesis. While training and evaluating GAN becomes increasingly …