We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs) , i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier’s predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.

中文翻译：

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KS(conf)：如果多类分类器在其规范之外运行，则进行轻量级测试

我们研究了自动检测给定的多类分类器是否在其规范之外（超出规范）运行的问题，即，输入数据来自与训练对象不同的分布。这是在为实际应用创建可靠的计算机视觉系统的道路上要解决的一个重要问题，因为如果分类器的预测质量不符合规范，则无法保证它的质量。先前提出的不合格检测方法在单个输入的级别上做出决策。然而，这不足以同时实现低误报率和高误报率。在这项工作中，我们描述了一个基于统计推理的名为 KS(conf) 的新过程。它的主要组成部分是经典的 Kolmogorov-Smirnov 检验，该检验应用于成批样本的预测置信度值集。使用批次而不是单个样本可以提高真阳性率而不会对假阳性率产生负面影响，从而克服单样本测试的关键限制。我们通过使用各种卷积网络架构和数据集的大量实验表明，即使在其他测试失败的情况下，KS(conf) 也能可靠地检测到超出规格的情况。此外，它还具有许多特性，使其成为实际部署的绝佳候选者：易于实现，几乎不增加系统开销，可与任何输出置信度分数的分类器一起使用，并且不需要关于数据如何进行的先验知识分布可能会改变。