Shared Knowledge Base for Multi Deep Learning in Defect Detection
Bibliographic record
Abstract
In recent years, there has been growing interest in applying deep learning techniques for visual anomaly detection, particularly in the manufacturing sector. Various models have been developed to identify defects in manufacturing data, yet selecting and optimizing these models for anomaly detection in intelligent manufacturing environments remains a significant challenge. This research focuses on general-purpose visual anomaly detection, aiming to reduce dependence on domain-specific knowledge and create flexible, generic models. We propose a novel deep learning framework in which multiple models are trained for each image. The visual features and loss values from these models are computed and stored during training. During the testing phase, this stored information is used to select the most appropriate model for each new image using a k-Nearest Neighbors (kNN) approach. The proposed method, KGDL-VAD (Knowledge-Guided Deep Learning for Visual Anomaly Detection), was evaluated on the MVTec AD, and standard aerospace defect detection datasets, achieving an area under the curve (AUC) score of 0.96, outperforming baseline methods. In addition, KGDL-VAD surpasses ensemble learning approaches across multiple domain-independent datasets with varying numbers of trained classes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 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".