Activation pruning of deep convolutional neural networks
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Bibliographic record
Abstract
Deep neural networks (DNNs) have risen to prominence, in the last few years, thanks to their very good performance on different classification and recognition tasks. However, their implementations suffer from long latency caused by the complexity of the network. Recently, many hardware implementations were introduced to accelerate the processing time of DNNs, and in particular of convolutional layers. While they can easily meet the timing constraint of real-time applications for small networks, complex models such as VGG and VGG-like networks are still out of reach. In this paper, we propose an technique to prune the neurons of each convolutional layer, also called activations, which directly contribute to the latency. Comparing networks with the same number of activations, we show that the activation-pruned networks perform better than the unpruned networks in terms of misclassification error.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it