Many-to-One: Transformer-based unsupervised anomaly detection and localization on industrial images
Bibliographic record
Abstract
Anomaly detection in computer vision-based quality control systems is crucial for industrial defect identification. This research presents the Many-to-One (M2O) framework, employing a multi-level transformer encoder and a single transformer decoder to detect and localize anomalies. With the advent of Industry 4.0 and electric vehicles, this area has gained significance. Despite prior contributions, challenges in generalization and time complexity persist. The M2O framework addresses these issues, enhancing robustness and efficiency in anomaly detection. M2O employs a transformer-based architecture and introduces the Multi-Level Feature Fuse module. To establish a benchmark for industrial electrical connectors, the ECAD dataset, containing real-world anomalies, is introduced. This dataset can inspire further research. Through evaluation against MVtec AD, BTAD, and ECAD, M2O demonstrates superior performance, overcoming previous limitations and offering a robust solution for industrial anomaly detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".