  Unsupervised anomaly detection with localization has many practical
applications when labeling is infeasible and, moreover, when anomaly examples
are completely missing in the train data. While recently proposed models for
such data setup achieve high accuracy metrics, their complexity is a limiting
factor for real-time processing. In this paper, we propose a real-time model
and analytically derive its relationship to prior methods. Our CFLOW-AD model
is based on a conditional normalizing flow framework adopted for anomaly
detection with localization. In particular, CFLOW-AD consists of a
discriminatively pretrained encoder followed by a multi-scale generative
decoders where the latter explicitly estimate likelihood of the encoded
features. Our approach results in a computationally and memory-efficient model:
CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art
with the same input setting. Our experiments on the MVTec dataset show that
CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by
1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source
our code with fully reproducible experiments.
