Multivariate Beta Normality Scores Approach for Deep Anomaly Detection in Images Using Transformations
Bibliographic record
Abstract
In this work, we propose a novel anomaly detection approach in images based on normality scores using transformations. By applying various transformations to the input image such as rotation and flipping, we train a classifier to predict the transformation label applied to the images. Then, we represent the output of the classifier by a softmax vector. Thanks to the flexibility of multivariate Beta in fitting the data compared to other conventional distributions such as the Dirichlet distribution, we approximate the softmax vector by this general form of the Beta distribution to construct the normality scores. Moreover, we use the Maximum Likelihood to estimate the parameters of the proposed distribution. To show the power and the effectiveness of our approach, we conduct experiments of detecting anomalies in various public datasets. Furthermore, the proposed method is compared with state-of-the-art techniques and results demonstrate its superiority in terms of Area Under Receiver Operating characteristics (AUROC).
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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 | 0.001 |
| 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".