Papers › Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

31 May 2021arXiv:2105.14737archive 2025-07-28

Jin-Hwa Kim, Do-Hyeong Kim, Saehoon Yi, Taehoon Lee

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.

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Code

jnhwkim/orthoad officialpytorch report
Pangoraw/SemiOrthogonal mentioned on GitHubpytorch report
Ultranity/Anomaly.Paddle mentioned on GitHubpaddleApache-2.0 report

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Tasks

Anomaly DetectionAnomaly SegmentationUnsupervised Anomaly Detectionfeature selection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection MVTec AD Semi-orthogonal Segmentation AUROC 98.2 #135 of 148 Archive leaderboard report
Unsupervised Anomaly Detection KolektorSDD Semi-orthogonal Segmentation AUROC 96.0 #1 of 1 Archive leaderboard report
Unsupervised Anomaly Detection KolektorSDD2 Semi-orthogonal Segmentation AUROC 98.1 #3 of 3 Archive leaderboard report

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