Substituting Convolutions for Neural Network Compression
Bibliographic record
Abstract
Many practitioners would like to deploy deep, convolutional neural networks in memory-limited scenarios, e.g., on an embedded device. However, with an abundance of compression techniques available it is not obvious how to proceed; many bring with them additional hyperparameter tuning, and are specific to particular network types. In this paper, we propose a simple compression technique that is general, easy to apply, and requires minimal tuning. Given a large, trained network, we propose (i) substituting its expensive convolutions with cheap alternatives, leaving the overall architecture unchanged; (ii) treating this new network as a student and training it with the original as a teacher through distillation. We demonstrate this approach separately for (i) networks predominantly consisting of full 3 ×3 convolutions and (ii) 1 ×1 or pointwise convolutions which together make up the vast majority of contemporary networks. We are able to leverage a number of methods that have been developed as efficient alternatives to fully-connected layers for pointwise substitution, allowing us provide Pareto-optimal benefits in efficiency/accuracy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".