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 …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Generating high-resolution, photo-realistic images has been a long-standing goal in
machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize …

This paper studies a curious phenomenon in learning energy-based model (EBM) using
MCMC. In each learning iteration, we generate synthesized examples by running a non …

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 …

A class of recent approaches for generating images, called Generative Adversarial Networks
(GAN), have been used to generate impressively realistic images of objects, bedrooms …

We propose the first stochastic framework to employ uncertainty for RGB-D saliency
detection by learning from the data labeling process. Existing RGB-D saliency detection …

We propose an energy-based model (EBM) in the latent space of a generator model, so that
the EBM serves as a prior model that stands on the top-down network of the generator …

Controllable generation is one of the key requirements for successful adoption of deep
generative models in real-world applications, but it still remains as a great challenge. In …

Zero-shot learning extends the conventional object classification to the unseen class
recognition by introducing semantic representations of classes. Existing approaches …