DICE: Leveraging Sparsification for Out-of-Distribution Detection

TL;DR

This paper introduces DICE, a sparsification-based framework that improves out-of-distribution detection by focusing on salient weights, reducing noise, and sharpening output distributions, with both theoretical and empirical validation.

Contribution

DICE is a novel sparsification approach that enhances OOD detection by selectively using important weights, providing theoretical insights and demonstrating competitive performance.

Findings

DICE reduces output variance for OOD data.

DICE sharpens output distribution for better separation.

DICE achieves competitive results on benchmarks.

Abstract

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. We demonstrate the effectiveness of sparsification-based OOD detection on several benchmarks and establish competitive performance.