Anomaly Detection with Test Time Augmentation and Consistency Evaluation

• URL: http://arxiv.org/abs/2206.02345v1
• Date: Mon, 6 Jun 2022 04:27:06 GMT
• Title: Anomaly Detection with Test Time Augmentation and Consistency Evaluation
• Authors: Haowei He, Jiaye Teng, Yang Yuan
• Abstract summary: We propose a simple, yet effective anomaly detection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD) We observe that in-distribution data enjoy more consistent predictions for its original and augmented versions on a trained network than out-distribution data. Experiments on various high-resolution image benchmark datasets demonstrate that TTA-AD achieves comparable or better detection performance.
• Score: 13.709281244889691
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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