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.

翻译：摘要：有效分布外（OOD）检测的关键在于获取鲁棒的分布内（ID）表征，使其与OOD样本区分开来。尽管以往方法主要依赖基于识别的方式实现此目标，但往往导致捷径学习，缺乏全面的表征。在本研究中，我们进行了全面分析，探索了不同的预训练任务并采用了多种OOD评分函数。结果表明，通过重建预训练获得的特征表征显著提升了性能，并缩小了不同评分函数之间的性能差距。这表明，当利用基于重建的前置任务时，即使简单的评分函数也能匹敌复杂的评分函数。基于重建的前置任务能够良好适配多种评分函数。因此，该方法具有进一步扩展的潜力。我们的OOD检测框架MOODv2采用掩码图像建模作为前置任务。在不添加额外复杂组件的情况下，MOODv2在ImageNet上将AUROC显著提升14.30%至95.68%，并在CIFAR-10上达到99.98%。