Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations

TL;DR

This paper introduces a method to improve out-of-distribution detection by measuring extreme activations in neural networks, effectively reducing overconfidence issues across various models and datasets.

Contribution

The authors propose using extreme activation values as a proxy for overconfidence, enhancing OOD detection performance without degrading accuracy.

Findings

Significant improvements in OOD detection AUC across multiple datasets and architectures.

Method does not negatively impact in-distribution classification performance.

Applicable to various neural network architectures and training scenarios.

Abstract

Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios. OOD inputs are commonly expected to cause a more uncertain prediction in the primary task; however, there are OOD cases for which the model returns a highly confident prediction. This phenomenon, denoted as "overconfidence", presents a challenge to OOD detection. Specifically, theoretical evidence indicates that overconfidence is an intrinsic property of certain neural network architectures, leading to poor OOD detection. In this work, we address this issue by measuring extreme activation values in the penultimate layer of neural networks and then leverage this proxy of overconfidence to improve on several OOD detection baselines. We test our method on a wide array of experiments spanning synthetic data and real-world data, tabular and image datasets, multiple architectures such as ResNet and Transformer, different training loss functions, and include the scenarios examined in previous theoretical work. Compared to the baselines, our method often grants substantial improvements, with double-digit increases in OOD detection AUC, and it does not damage performance in any scenario.