  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.
