  We present the efficiency of semi-orthogonal embedding for unsupervised
anomaly segmentation. The multi-scale features from pre-trained CNNs are
recently used for the localized Mahalanobis distances with significant
performance. However, the increased feature size is problematic to scale up to
the bigger CNNs, since it requires the batch-inverse of multi-dimensional
covariance tensor. Here, we generalize an ad-hoc method, random feature
selection, into semi-orthogonal embedding for robust approximation, cubically
reducing the computational cost for the inverse of multi-dimensional covariance
tensor. With the scrutiny of ablation studies, the proposed method achieves a
new state-of-the-art with significant margins for the MVTec AD, KolektorSDD,
KolektorSDD2, and mSTC datasets. The theoretical and empirical analyses offer
insights and verification of our straightforward yet cost-effective approach.
