Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

In recent years, the image and video coding technologies have advanced by leaps and
bounds. However, due to the popularization of image and video acquisition devices, the …

We present high quality image synthesis results using diffusion probabilistic models, a class
of latent variable models inspired by considerations from nonequilibrium thermodynamics …

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …

An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …

Learning useful representations without supervision remains a key challenge in machine
learning. In this paper, we propose a simple yet powerful generative model that learns such …

We describe an end-to-end trainable model for image compression based on variational
autoencoders. The model incorporates a hyperprior to effectively capture spatial …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Scene representation—the process of converting visual sensory data into concise
descriptions—is a requirement for intelligent behavior. Recent work has shown that neural …

We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …