Research map / Architectures and efficiency / Pruning and efficient tuning
DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the progressive structured-sparsity penalty (ablation) | DPFPS · CIFAR-10, VGG-Small | test accuracy: 93.01% -> 93.26% | Helped |
| add the progressive structured-sparsity penalty (ablation) | DPFPS · ImageNet, ResNet34 | Top-1 accuracy: 72.22% -> 72.25% | No clear effect |
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