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Status: Unchecked ×Keyword: channel independence ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   CHIP: CHannel Independence-based Pruning for Compact Neural Networks

   Sui, Yin, Xie, Phan, Zonouz and Yuan · arXiv (Cornell University) · 2021

   The paper proposes pruning neural network filters by measuring how independent each feature map is from the others, and reports that this compresses models with accuracy kept or improved.

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   1. UncheckedOn CIFAR-10, the CHIP pruning method raised accuracy by 0.90% (ResNet-56) and 0.94% (ResNet-110) while cutting model size and FLOPs by roughly 43-52%.“Notably, on CIFAR-10 dataset our solution can bring $0.90\%$ and $0.94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models, respectively, and meanwhile the model size and FLOPs are reduced by $42.8\%$ and $47.4\%$ (for ResNet-56) and $48.3\%$ and $52.1\%$ (for ResNet-110), respective…”
   2. UncheckedOn ImageNet, the pruning method cut ResNet-50 storage by 40.8% and computation by 44.8%, with accuracy 0.15% higher than the baseline.“On ImageNet dataset, our approach can achieve $40.8\%$ and $44.8\%$ storage and computation reductions, respectively, with $0.15\%$ accuracy increase over the baseline ResNet-50 model.”