Towards optimal sparse CNNs: sparsity-friendly knowledge distillation through feature decoupling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it