  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.
