An Active Learning Pipeline Powered by Image Synthetization and Retrieval Techniques: A Novel Training Approach for Construction-Centric DNNs
Bibliographic record
Abstract
Recognizing synthetic data’s role in advancing deep learning, this study explores its use in active learning, where systems autonomously identify and rectify their weaknesses. We introduce a novel synthetic oracle pipeline, integrating a Content-Based Image Retrieval (CBIR) module with an established image synthetization module (BlendCon) to create a synthetic oracle. To realize a synthetic-based active learning, the development of a high-performant synthetic oracle is a must. This oracle is crucial for replicating the detected model’s failures and refreshing training data, enhancing active learning effectiveness. We present a prototype of a synthetic oracle, demonstrating proficient image retrieval, generation, and visual quality verification. Our future work will focus on the development of the synthetic oracle and how it can be an enabler to the realization of synthetic-based deep active learning. Our visual verifications demonstrate the efficacy of the synthetic oracle and open novel avenues of research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".