Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
Bibliographic record
Abstract
In this study, we propose a filter pruning method based on MCP (Minimax Concave Penalty) regression. The convolutional process is conceptualized as a linear regression procedure, and the regression coefficients serve as indicators to assess the redundancy of channels. In the realm of feature selection, the efficacy of sparse penalized regression gradually outperforms that of Lasso regression. Building upon this insight, MCP regression is introduced to screen convolutional channels, coupled with the coordinate descent method, to effectuate model compression. In single-layer pruning and global pruning analyses, the Top1 loss value associated with the MCP regression compression method is consistently smaller than that of the Lasso regression compression method across diverse models. Specifically, when the global pruning ratio is set to 0.3, the Top1 accuracy of the MCP regression compression method, in comparison with that of the Lasso regression compression method, exhibits improvements of 0.21% and 1.67% under the VGG19_Simple and VGG19 models, respectively. Similarly, for ResNet34, at two distinct pruning ratios, the Top1 accuracy demonstrates enhancements of 0.33% and 0.26%. Lastly, we compare and discuss the novel methods introduced in this study, considering both time and space resource consumption.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".