Out-of-distribution detection is a common issue in deploying vision models in practice and solving it is an essential building block in safety critical applications. Existing OOD detection solutions focus on improving the OOD robustness of a classification model trained exclusively on in-distribution (ID) data. In this work, we take a different approach and propose to leverage generic pre-trained representations. We first investigate the behaviour of simple classifiers built on top of such representations and show striking performance gains compared to the ID trained representations. We propose a novel OOD method, called GROOD, that achieves excellent performance, predicated by the use of a good generic representation. Only a trivial training process is required for adapting GROOD to a particular problem. The method is simple, general, efficient, calibrated and with only a few hyper-parameters. The method achieves state-of-the-art performance on a number of OOD benchmarks, reaching near perfect performance on several of them. The source code is available at https://github.com/vojirt/GROOD.

翻译：分布外检测是视觉模型实际部署中的常见问题，解决该问题对于安全关键型应用至关重要。现有分布外检测方案主要聚焦于提升仅用分布内数据训练的分类模型的鲁棒性。本文另辟蹊径，提出利用通用预训练表征。我们首先研究了基于此类表征构建的简单分类器的行为特性，发现其性能相较于基于分布内训练表征的分类器有显著提升。我们提出名为GROOD的新型分布外检测方法，该方法通过使用优质通用表征实现卓越性能，且仅需少量训练过程即可适配特定问题。本方法具有简单、通用、高效、可校准的特点，仅涉及少量超参数。该方法在多个分布外检测基准上达到当前最优性能，部分基准实现近乎完美的检测效果。源代码发布于https://github.com/vojirt/GROOD。