Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD applications intend to further locate the anomalous regions without any anomaly information. Although the absence of anomalous samples and annotations deteriorates the UAD performance, an inconspicuous yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection and localization in an unsupervised fashion. The flow-based probabilistic models, only trained on anomaly-free data, can efficiently distinguish unpredictable anomalies by assigning them much lower likelihoods than normal data. Nevertheless, the size variation of unpredictable anomalies introduces another inconvenience to the flow-based methods for high-precision anomaly detection and localization. To generalize the anomaly size variation, we propose a novel Multi-Scale Flow-based framework dubbed MSFlow composed of asymmetrical parallel flows followed by a fusion flow to exchange multi-scale perceptions. Moreover, different multi-scale aggregation strategies are adopted for image-wise anomaly detection and pixel-wise anomaly localization according to the discrepancy between them. The proposed MSFlow is evaluated on three anomaly detection datasets, significantly outperforming existing methods. Notably, on the challenging MVTec AD benchmark, our MSFlow achieves a new state-of-the-art with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8%, and PRO score of 97.1%. The reproducible code is available at https://github.com/cool-xuan/msflow.

翻译：无监督异常检测（UAD）吸引了大量研究兴趣并推动了广泛应用，其训练过程中仅使用无异常样本。部分UAD应用旨在无任何异常信息的情况下进一步定位异常区域。尽管缺乏异常样本和标注会降低UAD性能，但归一化流这一不起眼却强大的统计模型，适用于无监督方式下的异常检测与定位。基于流的概率模型仅在无异常数据上训练，通过赋予不可预测异常远低于正常数据的似然值，能够有效区分异常。然而，不可预测异常的尺寸变化为基于流的方法实现高精度异常检测与定位带来了额外困难。为泛化异常尺寸变化，我们提出了一种新颖的多尺度流框架MSFlow，该框架由非对称并行流及后续的融合流组成，以交换多尺度感知信息。此外，根据图像级异常检测与像素级异常定位之间的差异，我们采用了不同的多尺度聚合策略。所提出的MSFlow在三个异常检测数据集上进行了评估，其性能显著优于现有方法。值得注意的是，在具有挑战性的MVTec AD基准上，我们的MSFlow达到了新的最优水平，检测AUROC分数高达99.7%，定位AUCROC分数为98.8%，PRO分数为97.1%。可复现代码已开源在https://github.com/cool-xuan/msflow。