Abstract
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 this https URL.
Abstract (translated)
分布不平衡检测是在实践中部署视觉模型的常见问题，并且解决它是安全关键应用中不可或缺的基本构建块。现有的分布不平衡检测解决方案专注于改善专门训练在分布(ID)数据上的分类模型的分布不平衡鲁棒性。在本研究中，我们采用了不同的方法，并提出了利用通用预训练表示的方法。我们首先研究了这些表示上简单的分类器的行为方式，并比ID训练表示表现出惊人的性能提升。我们提出了一种称为GROOD的新分布不平衡检测方法，它通过使用良好的通用表示实现卓越的性能。只需要一个简单的训练过程即可适应GROOD以特定问题。该方法简单、通用、高效、校准，且只需要几个超参数。该方法在多个分布不平衡基准测试中实现了最先进的性能，并在其中的几个测试中表现出几乎完美的性能。源代码可在这个httpsURL上获取。
URL
https://arxiv.org/abs/2303.13148
