Building and road detection from remote sensing images based on weights adaptive multi-teacher collaborative distillation using a fused knowledge
Bibliographic record
Abstract
Knowledge distillation is one effective approach to compress deep learning models. However, the current distillation methods are relatively monotonous. There are still rare studies about the combination of distillation strategies using multiple types of knowledge and employing multiple teacher models. Besides, how to optimize the weights among different teacher models is still an open problem. To address these issues, this paper proposes a novel approach for knowledge distillation, which effectively enhances the robustness of the distilled student model by a weights adaptive multi-teacher collaborative distillation. Moreover, the proposed method utilizes feature knowledge exchange guidance between teacher networks to transfer more comprehensive feature knowledge to the student model, which further improves the learning capability of hidden layers’ details. The extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on Massachusetts Roads Dataset, LRSNY Roads Dataset, and WHU Building Dataset. Specifically, under the guidance of the first ensemble of teacher networks, we obtained IoU scores of 47.33%, 78.15%, and 80.71%, respectively. Under the guidance of the second ensemble of teacher networks, we obtained IoU scores of 48.56%, 79.51%, and 81.35%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".