Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies
We propose a framework in which a classifier, trained on normal images and on synthetic anomalies, scores a test image without comparing it with normal data.
ProofPaper ↗
Key points
- Detectors of logical anomalies, such as missing, extra or wrongly combined parts, usually score a test image by its distance from normal (good) images.
- They achieve near-perfect image-level AUROC on standard industrial anomaly detection benchmarks such as MVTec LOCO, but their robustness to changes in capture conditions remains largely unexamined.
- Under a heterogeneous perturbation protocol in which each test image is randomly kept unaltered or subjected to a brightness reduction or a zoom-out transformation, the normal-reference methods we tested lose 9.7 to 34.1 AUROC points, and two of the strongest, CSAD and SALAD, fall from 97.7% and 96.4% to 68.1% and 62.3%.
- From a short description of each product, a language model derives anomaly types and editing instructions, an image editor applies them to normal images, and the classifier learns to separate the edited images from automatically discovered normal sub-modes.
Sources (1)
- [1]Robust Logical Anomaly Detection under Capture Shifts via Synthetic AnomaliesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 12:42 PM
We propose a framework in which a classifier, trained on normal images and on synthetic anomalies, scores a test image without comparing it with normal data.
Detectors of logical anomalies, such as missing, extra or wrongly combined parts, usually score a test image by its distance from normal (good) images.
Extractive summary: sentences quoted from the sources.