Papers › Knapsack Pruning with Inner Distillation
Knapsack Pruning with Inner Distillation
Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, Lihi Zelnik
Neural network pruning reduces the computational cost of an over-parameterized network to improve its efficiency. Popular methods vary from ℓ₁-norm sparsification to Neural Architecture Search (NAS). In this work, we propose a novel pruning method that optimizes the final accuracy of the pruned network and distills knowledge from the over-parameterized parent network's inner layers. To enable this approach, we formulate the network pruning as a Knapsack Problem which optimizes the trade-off between the importance of neurons and their associated computational cost. Then we prune the network channels while maintaining the high-level structure of the network. The pruned network is fine-tuned under the supervision of the parent network using its inner network knowledge, a technique we refer to as the Inner Knowledge Distillation. Our method leads to state-of-the-art pruning results on ImageNet, CIFAR-10 and CIFAR-100 using ResNet backbones. To prune complex network structures such as convolutions with skip-links and depth-wise convolutions, we propose a block grouping approach to cope with these structures. Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by 1% and 0.3% respectively on ImageNet, and faster runtime on GPU.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Network Pruning | ImageNet | ResNet50 2.5 GFLOPS | Accuracy | 78.0 | #3 of 16 | Archive leaderboard | report |
| Network Pruning | ImageNet | ResNet50 2.5 GFLOPS | GFLOPs | 2.5 | #3 of 16 | Archive leaderboard | report |
| Network Pruning | ImageNet | ResNet50 2.0 GFLOPS | Accuracy | 77.70 | #5 of 16 | Archive leaderboard | report |
| Network Pruning | ImageNet | ResNet50 2.0 GFLOPS | GFLOPs | 2 | #5 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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