Background. Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a distribution similar to that of the training set. However, DNNs' predictions are brittle and unreliable when the test samples are drawn from a dissimilar distribution. This is a major concern for deployment in real-world applications, where such behavior may come at a considerable cost, such as industrial production lines, autonomous vehicles, or healthcare applications. Contributions. We frame Out Of Distribution (OOD) detection in DNNs as a statistical hypothesis testing problem. Tests generated within our proposed framework combine evidence from the entire network. Unlike previous OOD detection heuristics, this framework returns a $p$-value for each test sample. It is guaranteed to maintain the Type I Error (T1E - mistakenly identifying OOD samples as ID) for test data. Moreover, this allows combining several detectors while maintaining the T1E. Building on this framework, we suggest a novel OOD procedure based on low-order statistics. Our method achieves comparable or better results than state-of-the-art methods on well-accepted OOD benchmarks, without retraining the network parameters or assuming prior knowledge on the test distribution -- and at a fraction of the computational cost.
翻译:通常情况下,深神经网络(DNNS)对从与培训组相似的分布分布中提取的样本进行全面概括;然而,当测试样品从不同分布中提取时,DNNS的预测是微弱和不可靠的,这是在实际应用中部署的重大关切问题,因为这种行为可能以相当大的成本出现,如工业生产线、自主车辆或保健应用等。 贡献。我们将DNNS的分发(OOOD)检测作为统计假设测试问题,在我们拟议框架内产生的检测将来自整个网络的证据综合起来。与以前的OOOD检测超常不同,这个框架对每个测试样本的回报值为1美元。保证在测试数据中保留I类错误(T1E -- -- 错误地识别OOD样品为ID)。此外,这样可以将几个探测器结合起来,同时维护T1E。基于这个框架,我们建议一种基于低级统计的新型OOD程序。我们的方法比完全接受OD标准的最新方法取得可比或更好的结果。