Evaluation of Pruning Techniques
Bibliographic record
Abstract
CNNs are widely used in a variety of computer vision tasks such as image processing, image classification, etc. The state-of-the-art neural networks are bigger and have greater number of parameters which translates to greater computations and memory footprint. This makes it difficult to deploy the real-time applications using these models on resource constrained edge devices. To address this issue, pruning techniques have been explored which reduce the number of computations and memory requirements of modern CNNs with negligible loss in accuracy. In this paper, we analyze the performance of three different pruning techniques - L1-norm based filter pruning, channel pruning and weight pruning and compare the performance in terms of inference time and accuracy. We also compared the performance of the pruned networks on two different GPU architectures and found that the inference time of pruned networks is more boosted in NVIDIA V100 compared to NVIDIA GTX1080 Ti due to its superior architecture features. We also perform inference speedup comparison between the different pruning techniques and analyze performance benefits between the different pruning techniques.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".