Teacher−student framework‐based knowledge transfer with incremental block‐wise retraining
Bibliographic record
Abstract
This Letter proposes a stage‐wise training method that uses block‐wise retraining to transfer the useful knowledge of a pre‐trained deep residual network (ResNet) in a teacher−student framework (TSF). To achieve this, flow‐based hidden information transfer and hierarchically supervised retraining of the information are alternatively implemented from bottom to top between teacher and student ResNets in the TSF. To evaluate the effectiveness of the proposed method, the authors used well‐known image data sets Canadian Institute For Advanced Research (CIFAR)‐10, CIFAR‐100, and street view house number. The results showed that the flow‐based bottom‐up knowledge transfer combined with incremental block‐wise retraining can provide the improved small student ResNet with higher accuracy than the deep teacher ResNet. This approach will help extend the use of deep neural network models to limited computing environments.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".