In this paper, we present SemSAD, a simple and generic framework for detecting examples that lie out-of-distribution (OOD) for a given training set. The approach is based on learning a semantic similarity measure to find for a given test example the semantically closest example in the training set and then using a discriminator to classify whether the two examples show sufficient semantic dissimilarity such that the test example can be rejected as OOD. We are able to outperform previous approaches for anomaly, novelty, or out-of-distribution detection in the visual domain by a large margin. In particular, we obtain AUROC values close to one for the challenging task of detecting examples from CIFAR-10 as out-of-distribution given CIFAR-100 as in-distribution, without making use of label information

翻译：在本文中,我们介绍SemSAD,这是一个简单和通用的框架,用于发现某一培训组中超出分配范围(OOOD)的范例,这个框架是一个简单和通用的框架,其基础是学习一种语义相似性措施,为某一试验实例找到训练组中最接近语义的例子,然后使用歧视者对这两个例子是否显示出足够的语义差异性,从而可以以OOOD的形式拒绝试验示例。我们能够大大超越以前在视觉领域对异常、新奇或分配范围以外的探测方法。特别是,我们获得AUROC的接近于AUROC的数值,以完成一项艰巨的任务,即探测来自CIFAR-10的事例,作为向CIFAR-100提供的在分配中的超出分配范围,不使用标签信息。