Novel Norms for Pruning Convolutional Neural Networks
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) for object detection in images can have millions of parameters, leading to a large memory footprint and long inference times. These parameters are grouped into objects called filters. Structured pruning is the process of removing some proportion of these filters in a CNN without significantly reducing its accuracy. The manner in which filters are chosen affects the performance of the model. One method for selecting which filters to prune is to use a norm function to assign a numeric value to each filter and prune the ones with the smallest values. Existing works only use a handful of norms for pruning. In this work, we evaluate the effectiveness of a wide range of norms for pruning, including novel norms that we have developed. We compare the results of our evaluation and show that our norms are more effective for pruning than conventional norms for the YOLOv10n model. Our methods for evaluating norms can be used by researchers with other models they may wish to evaluate, and with the norms discussed in this paper as well as norms they may define themselves.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".