Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification
Bibliographic record
Abstract
Unsupervised anomaly detection and localization in images is a challenging problem, leading previous methods to attempt an easier supervised one-class classification formalization. Assuming training images to be realizations of the underlying image distribution, it follows that nominal patches from these realizations will be well associated between and represented across realizations. From this, we propose Inter-Realization Channels (InReaCh), a fully unsupervised method of detecting and localizing anomalies. InReaCh extracts high-confidence nominal patches from training data by associating them between realizations into channels, only considering channels with high spans and low spread as nominal. We then create our nominal model from the patches of these channels to test new patches against. InReaCh extracts nominal patches from the MVTec AD dataset with 99.9% precision, then archives 0.968 AUROC in localization and 0.923 AUROC in detection with corrupted training data, competitive with current state-of-the-art supervised one-class classification methods. We test our model up to 40% of training data containing anomalies with negligibly affected performance. The shift to fully unsupervised training simplifies dataset creation and broadens possible applications. Code: github.com/DeclanMcIntosh/InReaCh
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".