  For a long time, anomaly localization has been widely used in industries.
Previous studies focused on approximating the distribution of normal features
without adaptation to a target dataset. However, since anomaly localization
should precisely discriminate normal and abnormal features, the absence of
adaptation may make the normality of abnormal features overestimated. Thus, we
propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes
sophisticated anomaly localization using features adapted to the target
dataset. CFA consists of (1) a learnable patch descriptor that learns and
embeds target-oriented features and (2) scalable memory bank independent of the
size of the target dataset. And, CFA adopts transfer learning to increase the
normal feature density so that abnormal features can be clearly distinguished
by applying patch descriptor and memory bank to a pre-trained CNN. The proposed
method outperforms the previous methods quantitatively and qualitatively. For
example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in
anomaly localization of MVTec AD benchmark. In addition, this paper points out
the negative effects of biased features of pre-trained CNNs and emphasizes the
importance of the adaptation to the target dataset. The code is publicly
available at https://github.com/sungwool/CFA_for_anomaly_localization.
