Deep neural networks are known to be vulnerable to unseen data: they may
wrongly assign high confidence stcores to out-distribuion samples. Recent works
try to solve the problem using representation learning methods and specific
metrics. In this paper, we propose a simple, yet effective post-hoc anomaly
detection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD),
inspired by a novel observation. Specifically, we observe that in-distribution
data enjoy more consistent predictions for its original and augmented versions
on a trained network than out-distribution data, which separates
in-distribution and out-distribution samples. Experiments on various
high-resolution image benchmark datasets demonstrate that TTA-AD achieves
comparable or better detection performance under dataset-vs-dataset anomaly
detection settings with a 60%~90\% running time reduction of existing
classifier-based algorithms. We provide empirical verification that the key to
TTA-AD lies in the remaining classes between augmented features, which has long
been partially ignored by previous works. Additionally, we use RUNS as a
surrogate to analyze our algorithm theoretically.

本文提出一种针对神经网络对未知数据的检测算法，名为 TTA-AD，借鉴了一种新的观察方法，实验证明 TTA-AD 在不同高效图像基准数据集下，运行时间比现有基于分类器的算法下降了 60%~90% 且具有可比较或更好的检测性能。