Learning Confidence for Out-of-Distribution Detection in Neural Networks

TL;DR

This paper introduces a simple, interpretable method for neural networks to estimate confidence levels, improving out-of-distribution detection without extra labels or out-of-distribution data, and explores calibration techniques using misclassified in-distribution examples.

Contribution

The authors propose a novel confidence learning approach that enhances out-of-distribution detection and calibration, outperforming existing methods without requiring additional data.

Findings

Outperforms recent confidence-based OOD detection techniques

Does not require extra labels or out-of-distribution examples

Uses misclassified in-distribution examples for calibration

Abstract

Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safely perform. To jointly address these issues, we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs. We demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network's output distribution, without requiring any additional labels or access to out-of-distribution examples. Additionally, we address the problem of calibrating out-of-distribution detectors, where we demonstrate that misclassified in-distribution examples can be used as a proxy for out-of-distribution examples.