Covariate Shift Detection via Domain Interpolation Sensitivity
Covariate Shift Detection via Domain Interpolation Sensitivity
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发表时间：
2022
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通讯作者：
Tejas Gokhale;Joshua Forster Feinglass
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作者：
Tejas Gokhale;Joshua Forster Feinglass
协变量转移是现实世界中图像分类器可靠性的主要障碍。关于协变量转移的工作一直集中在培训分类器上，以适应或推广到看不见的域。但是，对于透明的决策，同样需要开发可以指示测试图像是否属于看不见的域的协方差转移检测方法。在本文中，我们介绍了协变量转移检测（CSD）的基准，该基础是基于域概括的先前工作。我们将最新的OOD检测方法用作基准，发现它们比我们CSD基准上的基于置信的方法要差。我们提出了一种基于插值的技术，域插值灵敏度（DIS），基于一个简单的假设，即测试输入和从训练域中随机采样输入之间的插值提供了足够的信息来区分训练域和在协方差下的互面域和看不见的域。 DIS超过了多个域概括基准上CSD的所有OOD检测基线。
Covariate shift is a major roadblock in the reliability of image classifiers in the real world. Work on covariate shift has been focused on training classifiers to adapt or generalize to unseen domains. However, for transparent decision making, it is equally desirable to develop covariate shift detection methods that can indicate whether or not a test image belongs to an unseen domain. In this paper, we introduce a benchmark for covariate shift detection (CSD), that builds upon and complements previous work on domain generalization. We use state-of-the-art OOD detection 1 methods as baselines and find them to be worse than simple confidence-based methods on our CSD benchmark. We propose an interpolation-based technique, Domain Interpolation Sensitivity (DIS), based on the simple hypothesis that interpolation between the test input and randomly sampled inputs from the training domain, offers sufficient information to distinguish between the training domain and unseen domains under covariate shift. DIS surpasses all OOD detection baselines for CSD on multiple domain generalization benchmarks.