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Keyword: iterative pruning ×Clear all

2 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks

   Ding, Ding, Han and Tang · Proceedings of the AAAI Conference on Artificial Intelligence · 2018

   The paper proposes Auto-balanced Filter Pruning, which removes redundant filters from convolutional networks to shrink them and cut computation, reportedly without obvious accuracy loss.

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   1. UncheckedThe authors report cutting inference cost by 95.1% for LeNet-5, 79.7% for VGG-16 and 60.9% for ResNet-56 using their filter pruning method.“Concretely, we reduce the inference cost of LeNet-5 on MNIST, VGG-16 and ResNet-56 on CIFAR-10 by 95.1%, 79.7% and 60.9%, respectively.”

2. Computer Science › Advanced Neural Network Applications

   Pruning via Iterative Ranking of Sensitivity Statistics

   Verdenius, Stol and Forré · arXiv (Cornell University) · 2020

   The paper introduces SNIP-it, which prunes iteratively using SNIP's sensitivity criterion, for structured and unstructured pruning, and reports state-of-the-art sparsity-performance trade-offs.

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   1. UncheckedApplying SNIP's sensitivity criterion repeatedly in smaller steps, still before training, can improve pruning performance without a hard implementation.“However, in this work we show that by applying the sensitivity criterion iteratively in smaller steps - still before training - we can improve its performance without difficult implementation.”