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Keyword: L-BFGS ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Adaptive Filter Pruning via Sensitivity Feedback

   Zhang and Freris · IEEE Transactions on Neural Networks and Learning Systems · 2023

   The paper proposes ASTER, a filter-pruning method that adjusts its pruning threshold during training using loss sensitivity, and reports reduced FLOPs with small accuracy drops on several CNNs.

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   1. UncheckedASTER estimates how the loss responds to its pruning threshold during training, without retraining, by applying L-BFGS only to batch normalization layers.“ASTER computes the sensitivity of the loss to the threshold on the fly (without retraining); this is carried efficiently by an application of L-BFGS solely on the batch normalization (BN) layers.”
   2. UncheckedOn MobileNet v3-small, a very lightweight image classifier, the ASTER pruning method cuts 16.1% of FLOPs while Top-1 accuracy falls by only 0.03%.“Even for a very lightweight classification model like MobileNet v3-small, ASTER saves 16.1% FLOPs with a negligible Top-1 accuracy drop of 0.03%.”