Semi-supervised Anomaly Detection using AutoEncoders
Bibliographic record
Abstract
Anomaly detection refers to the task of finding unusual instancesthat stand out from the normal data. In several applications, theseoutliers or anomalous instances are of greater interest compared tothe normal ones. Specifically in the case of industrial optical inspection and infrastructure asset management, finding these defects(anomalous regions) is of extreme importance. Traditionally andeven today this process has been carried out manually. Humansrely on the saliency of the defects in comparison to the normal texture to detect the defects. However, manual inspection is slow, tedious, subjective and susceptible to human biases. Therefore, theautomation of defect detection is desirable. But for defect detectionlack of availability of a large number of anomalous instances andlabelled data is a problem. In this paper, we present a convolutionalauto-encoder architecture for anomaly detection that is trained onlyon the defect-free (normal) instances. For the test images, residual masks that are obtained by subtracting the original image fromthe auto-encoder output are thresholded to obtain the defect segmentation masks. The approach was tested on two data-sets andachieved an impressive average F1 score of 0.885. The networklearnt to detect the actual shape of the defects even though no defected images were used during the training.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".