Conjugate Energy-Based Models

Abstract

We propose conjugate energy-based models (EBMs), a class of deep latent-variable models with a tractable posterior. Conjugate EBMs have similar use cases as variational autoencoders, in the sense that they learn an unsupervised mapping between data and latent variables. However these models omit a generator, which allows them to learn more flexible notions of similarity between data points. Our experiments demonstrate that conjugate EBMs achieve competitive results in terms of image modelling, predictive power of latent space, and out-of-distribution detection on a variety of datasets.

Publication
3rd Symposium on Advances in Approximate Bayesian Inference