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

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Out-of-Distribution Detection ImageNet-1k vs Curated OODs (avg.) MOOD AUROC 89.1 # 13
Out-of-Distribution Detection ImageNet-1k vs iNaturalist MOOD AUROC 86.9 # 18
Out-of-Distribution Detection ImageNet-1k vs Places MOOD AUROC 88.5 # 12
Out-of-Distribution Detection ImageNet-1k vs SUN MOOD AUROC 89.8 # 12
Out-of-Distribution Detection ImageNet-1k vs Textures MOOD AUROC 91.3 # 15


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