Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders

TL;DR

This paper introduces the Explaining-Away VAE (EA-VAE), an extension to standard VAEs that improves uncertainty representation in computer vision tasks by incorporating a global scaling latent variable, inspired by classical vision and neural normalization.

Contribution

The paper proposes a simple yet effective modification to VAEs using a global scaling latent variable, enhancing uncertainty estimation across diverse vision datasets and conditions.

Findings

EA-VAE restores normative uncertainty requirements in vision tasks.

The introduced scaling latent improves contrast and out-of-distribution uncertainty handling.

EA-VAE employs divisive normalization, akin to biological neural networks, to enhance inference.

Abstract

Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), have the potential to meet this demand by building latent representations that learn to associate uncertainties with inferences while avoiding their characteristic intractable computations. Yet, we show that it is precisely uncertainty representation that suffers from inconsistencies under an array of relevant computer vision conditions: contrast-dependent computations, image corruption, out-of-distribution detection. Drawing inspiration from classical computer vision, we present a principled extension to the standard VAE by introducing a simple yet powerful inductive bias through a global scaling latent variable, which we call the Explaining-Away VAE (EA-VAE). By applying EA-VAEs to a spectrum of computer vision domains and a variety of datasets, spanning standard NIST datasets to rich medical and natural image sets, we show the EA-VAE restores normative requirements for uncertainty. Furthermore, we provide an analytical underpinning of the contribution of the introduced scaling latent to contrast-related and out-of-distribution related modulations of uncertainty, demonstrating that this mild inductive bias has stark benefits in a broad set of problems. Moreover, we find that EA-VAEs recruit divisive normalization, a motif widespread in biological neural networks, to remedy defective inference. Our results demonstrate that an easily implemented, still powerful update to the VAE architecture can remedy defective inference of uncertainty in probabilistic computations.