General Frameworks for Anomaly Detection Explainability: Comparative Study
Bibliographic record
Abstract
Since their inception, AutoEncoders have been very important in representational learning. They have achieved ground-breaking results in the realm of automated unsupervised anomaly detection for various critical applications. However, anomaly detection through AutoEncoders suffers from lack of transparency when it comes to decision making based on the outputs of the AutoEncoder network, especially for image-based models. Though the residual reconstruction error map from the AutoEncoder helps explaining anomalies to a certain extent, it is not a good indicator of the implicitly learnt attributes by the model. A human interpretable explanation of why an instance is anomalous not only enables the experts to fine-tune the model but also establishes and increases trust by non-expert users of the model. Convolutional AutoEncoders in particular suffer the most as there are only limited studies that focus on transparency and explainability. In this paper, aiming to bridge this gap, we explore the feasibility and compare the performances of several State-of-the-Art Explainable Artificial Intelligence (XAI) frameworks on Convolutional AutoEncoders. The paper also aims at providing the basis for future developments of reliable and trustworthy AutoEncoders for visual 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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".