Papers › Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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