  The crux of effective out-of-distribution (OOD) detection lies in acquiring a
robust in-distribution (ID) representation, distinct from OOD samples. While
previous methods predominantly leaned on recognition-based techniques for this
purpose, they often resulted in shortcut learning, lacking comprehensive
representations. In our study, we conducted a comprehensive analysis, exploring
distinct pretraining tasks and employing various OOD score functions. The
results highlight that the feature representations pre-trained through
reconstruction yield a notable enhancement and narrow the performance gap among
various score functions. This suggests that even simple score functions can
rival complex ones when leveraging reconstruction-based pretext tasks.
Reconstruction-based pretext tasks adapt well to various score functions. As
such, it holds promising potential for further expansion. Our OOD detection
framework, MOODv2, employs the masked image modeling pretext task. Without
bells and whistles, MOODv2 impressively enhances 14.30% AUROC to 95.68% on
ImageNet and achieves 99.98% on CIFAR-10.
