Out-of-distribution detection for regression tasks: parameter versus predictor entropy

TL;DR

This paper investigates out-of-distribution detection for regression tasks, comparing parameter and predictor entropy, and introduces a new entropy estimation method using nearest neighbors to improve OOD detection robustness.

Contribution

It proposes a novel entropy estimation approach based on nearest neighbors in function space and demonstrates its effectiveness for OOD detection in regression.

Findings

Parameter diversity may not ensure predictor diversity

The new entropy estimation improves OOD detection accuracy

The method is robust across different OOD scenarios

Abstract

It is crucial to detect when an instance lies downright too far from the training samples for the machine learning model to be trusted, a challenge known as out-of-distribution (OOD) detection. For neural networks, one approach to this task consists of learning a diversity of predictors that all can explain the training data. This information can be used to estimate the epistemic uncertainty at a given newly observed instance in terms of a measure of the disagreement of the predictions. Evaluation and certification of the ability of a method to detect OOD require specifying instances which are likely to occur in deployment yet on which no prediction is available. Focusing on regression tasks, we choose a simple yet insightful model for this OOD distribution and conduct an empirical evaluation of the ability of various methods to discriminate OOD samples from the data. Moreover, we exhibit evidence that a diversity of parameters may fail to translate to a diversity of predictors. Based on the choice of an OOD distribution, we propose a new way of estimating the entropy of a distribution on predictors based on nearest neighbors in function space. This leads to a variational objective which, combined with the family of distributions given by a generative neural network, systematically produces a diversity of predictors that provides a robust way to detect OOD samples.