The out-of-distribution (OOD) detection task is crucial for the real-world deployment of machine learning models. In this paper, we propose to study the problem from the perspective of Sharpness-aware Minimization (SAM). Compared with traditional optimizers such as SGD, SAM can better improve the model performance and generalization ability, and this is closely related to OOD detection 1. Therefore, instead of using SGD, we propose to fine-tune the model with SAM, and observe that the score distributions of in-distribution (ID) data and OOD data are pushed away from each other. Besides, with our carefully designed loss, the fine-tuning process is very time-efficient. The OOD performance improvement can be observed after fine-tuning the model within 1 epoch. Moreover, our method is very flexible and can be used to improve the performance of different OOD detection methods. Extensive experiments have demonstrated that our method achieves state-of-the-art performance on widely-used OOD benchmarks across different architectures. Moreover, comprehensive ablation studies and theoretical analyses are discussed to support the empirical results.
Zhang et al. (Thu,) studied this question.