A Less Biased Evaluation of Out-of-distribution Sample Detectors
Abstract
In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictable behaviour. We need to know whether any given input belongs to the population distribution of the training/evaluation data to prevent unpredictable behaviour in deployed systems. A recent surge of interest in this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standard problem definition or an exhaustive evaluation, it is not evident if we can rely on these methods. What makes this problem different from a typical supervised learning setting is that the distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a more reliable strategy to assess progress on this problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Contrary to the
中文速览
现有深度学习系统在遇到训练分布之外的输入（out-of-distribution，OOD样本）时会产生不可预测的输出，因此需要一种可靠的方法来判断某个输入是否属于训练数据的分布范围。以往的评估方式只用两个数据集——一个正常样本集和一个异常样本集——来训练并测试检测器，这会导致模型对特定异常集过拟合，给出过于乐观的结果。本文提出了OD-test，一种引入第三个"目标异常集"的三数据集评估框架：用一个异常集训练检测器，再用另一个从未见过的异常集来测试，从而更真实地模拟实际部署场景中的不确定性。作者在多个复杂度递增的图像分类任务上系统评测了来自不确定性估计、密度估计、异常检测等领域的多种方法，结果表明在高维图像上这些方法的准确率普遍接近随机猜测基线，说明当前技术在实际应用中远没有已有文献所声称的那样可靠，迫切需要更严格的基准评估标准。
原文 arXiv:1809.04729;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1809.04729v2