Knowledge in attention assistant for improving generalization in deep teacher–student models
Bibliographic record
Abstract
Research on knowledge distillation has become active in deep neural networks. Knowledge distillation involves training a low-capacity model from a high-capacity model. However, when the capacities of the teacher and student models differ, it can result to poor learning and low generalization performance. We propose here a novel teacher assistant model called Knowledge in Attention Assistant. This model learns learning a discriminative representation of important regions and statistical information along with spatial and channel knowledge. Moreover, by using a triplet attention mechanism, the student model can learn both the inner and outer distribution of different categories, and also memorize the knowledge distribution of the teacher model. This alignment improves the effectiveness and generalization of knowledge distillation and reduces the capacity gap between the teacher and student models. The present model addresses feature inconsistency by adjusting the attention weight distribution based on the resemblance between the features of the teacher and student. The evaluation of the proposed teacher assistant method shows remarkable results. The student model outperforms the teacher model in terms of generalization performance, achieving improvements of 93.37% and 94.09% on CIFAR-10 and CIFAR-100 datasets, respectively. Furthermore, the proposed model enhances the F1-scores 91.98% on CIFAR-10 and 79.69% on CIFAR-100.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".