AutoEncoder regularization using Support Vector Data Description for Anomaly Detection
Bibliographic record
Abstract
In computer vision, learning discriminative features to detect anomalies in images is a challenge. The majority of deep learning-based image anomaly detection approaches are compression-reconstruction or generation-based models that were not initially intended for the anomaly detection task. Only a few methods involve dedicated objective function to help detect anomalies and they are not visually explainable as well as reconstruction based approaches. Though the popular reconstruction-based approach for anomaly detection using Convolutional AutoEncoder has achieved the state of the art results, there is no provision to induce the fabrication of discriminatively learnt embeddings from the inputs to well reflect anomalies in the output. We propose an approach using Support Vector Data Description as a regularizer to enforce discriminative ability to easily segregate anomalies from normality with little effort in modelling and tuning the AutoEncoders in our work. We evaluate our approach on several visual anomaly detection datasets to show the capability of our approach. We also perform extensive ablation studies for efficient tuning of parameters.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".