Out-of-distribution (OOD) detection is important for deploying reliable
machine learning models on real-world applications. Recent advances in outlier
exposure have shown promising results on OOD detection via fine-tuning model
with informatively sampled auxiliary outliers. However, previous methods assume
that the collected outliers can be sufficiently large and representative to
cover the boundary between ID and OOD data, which might be impractical and
challenging. In this work, we propose a novel framework, namely, Diversified
Outlier Exposure (DivOE), for effective OOD detection via informative
extrapolation based on the given auxiliary outliers. Specifically, DivOE
introduces a new learning objective, which diversifies the auxiliary
distribution by explicitly synthesizing more informative outliers for
extrapolation during training. It leverages a multi-step optimization method to
generate novel outliers beyond the original ones, which is compatible with many
variants of outlier exposure. Extensive experiments and analyses have been
conducted to characterize and demonstrate the effectiveness of the proposed
DivOE. The code is publicly available at: https://github.com/tmlr-group/DivOE.Comment: accepted by NeurIPS 202