Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need

• URL: http://arxiv.org/abs/2302.02615v2
• Date: Tue, 11 Apr 2023 12:46:52 GMT
• Title: Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
• Authors: Jingyao Li, Pengguang Chen, Shaozuo Yu, Zexin He, Shu Liu, Jiaya Jia
• Abstract summary: We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
• Score: 52.88953913542445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-based methods to learn the ID features, which tend to learn shortcuts instead of comprehensive representations. In this work, we find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We deeply explore the main contributors of OOD detection and find that reconstruction-based pretext tasks have the potential to provide a generally applicable and efficacious prior, which benefits the model in learning intrinsic data distributions of the ID dataset. Specifically, we take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD). Without bells and whistles, MOOD outperforms previous SOTA of one-class OOD detection by 5.7%, multi-class OOD detection by 3.0%, and near-distribution OOD detection by 2.1%. It even defeats the 10-shot-per-class outlier exposure OOD detection, although we do not include any OOD samples for our detection

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