  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
