Towards optimal sparse CNNs: sparsity-friendly knowledge distillation through feature decoupling
Bibliographic record
Abstract
Despite the efficacy of network sparsity in reducing the complexity of convolutional neural networks (CNNs), the performance of sparse networks often deteriorates significantly compared to their dense counterparts. Knowledge distillation is regarded as a potent strategy for utilizing large models to augment the performance of smaller counterparts; however, its advantages for sparse networks remain substantially constrained. We identify in this article that the underlying issue stems from sparse student models exhibiting disparate behaviors in processing foreground and background features, thereby hindering the uniform transfer of knowledge from dense models that address both feature types concurrently. Building on this insight, we introduce a novel sparsity-friendly knowledge distillation (SF-KD) method, which independently supervises the two feature types using feature decoupling to facilitate effective knowledge distillation for sparse networks. Specifically, we decouple the foreground and background features through unique pooling techniques and implement separate mean squared error (MSE) feature distillation. Furthermore, we dynamically adjust the weights of the two loss components to optimize performance. Experimental results on Canadian Institute For Advanced Research (CIFAR) datasets (including CIFAR-10 and CIFAR-100) and Mini-ImageNet benchmarks substantiate significant performance enhancements, underscoring the effectiveness of our proposed methodology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".