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

Understand

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.