Papers › Anomaly Detection via Reverse Distillation from One-Class Embedding
Anomaly Detection via Reverse Distillation from One-Class Embedding
Hanqiu Deng, Xingyu Li
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD).The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD. However, using similar or identical architectures to build the teacher and student models in previous studies hinders the diversity of anomalous representations. To tackle this problem, we propose a novel T-S model consisting of a teacher encoder and a student decoder and introduce a simple yet effective "reverse distillation" paradigm accordingly. Instead of receiving raw images directly, the student network takes teacher model's one-class embedding as input and targets to restore the teacher's multiscale representations. Inherently, knowledge distillation in this study starts from abstract, high-level presentations to low-level features. In addition, we introduce a trainable one-class bottleneck embedding (OCBE) module in our T-S model. The obtained compact embedding effectively preserves essential information on normal patterns, but abandons anomaly perturbations. Extensive experimentation on AD and one-class novelty detection benchmarks shows that our method surpasses SOTA performance, demonstrating our proposed approach's effectiveness and generalizability.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | GoodsAD | RD4AD | AUPR | 68.2 | #8 of 11 | Archive leaderboard | report |
| Anomaly Classification | GoodsAD | RD4AD | AUROC | 66.5 | #8 of 11 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-S | ReverseDistillation | Detection AUROC | 81.0 | #3 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-S | ReverseDistillation | Segmentation AUPRO | 85.6 | #3 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-V | ReverseDistillation | Detection AUROC | 71.0 | #2 of 7 | Archive leaderboard | report |
| Anomaly Detection | Fashion-MNIST | Reverse Distillation | ROC AUC | 95.0 | #3 of 12 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Reverse Distillation | Detection AUROC | 98.5 | #57 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Reverse Distillation | Segmentation AUPRO | 93.9 | #57 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Reverse Distillation | Segmentation AUROC | 97.8 | #57 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | RD4AD | Avg. Detection AUROC | 78.7 | #29 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | RD4AD | Detection AUROC (only logical) | 69.4 | #29 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | RD4AD | Detection AUROC (only structural) | 88.0 | #29 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | RD4AD | Segmentation AU-sPRO (until FPR 5%) | 63.7 | #29 of 40 | Archive leaderboard | report |
| Anomaly Detection | One-class CIFAR-10 | Reverse Distillation | AUROC | 86.5 | #24 of 36 | Archive leaderboard | report |
| Anomaly Detection | VisA | Reverse Distillation | Segmentation AUPRO (until 30% FPR) | 70.9 | #45 of 50 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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