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Status: Unchecked ×Keyword: adaptive regularization ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration

   Shi, Xu, Tasaka and Chen · IEEE Transactions on Circuits and Systems for Video Technology · 2020

   The paper proposes SASL, which adjusts sparsity regularisation per filter by saliency to prune CNNs, and reports cutting 49.7% of ResNet-50's FLOPs with small accuracy loss on ILSVRC-2012.

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   1. UncheckedThe paper says that, when pruning, using hard sample mining to optimise its data-dependent criterion is more effective and efficient.“During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency.”
   2. UncheckedThe authors report that their pruning method cuts ResNet-50's computation (FLOPs) by 49.7% on ILSVRC-2012, with accuracy drops of 0.39% (top-1) and 0.05% (top-5).“Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation.”