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Keyword: mobile device deployment ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Progressive Kernel Pruning Based on the Information Mapping Sparse Index for CNN Compression

   Zhu, Zhao and Pei · IEEE Access · 2021

   The paper proposes progressive kernel pruning, guided by an information mapping sparse index, to compress CNNs, and reports better compression on CIFAR-10/100 and ImageNet than existing methods.

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   1. UncheckedPruning kernels inside filters first, then in whole layers, is said to exploit easy optimisation and avoid local optima that direct layer-level pruning risks.“Progressive kernel pruning can make use of the characteristic that the model is easy to optimize when kernel pruning in the filter, and it avoids having the model easily fall into local optima when kernel pruning in the layer directly.”
   2. UncheckedOn CIFAR-10/100 and ImageNet, the authors' IMSI-based progressive kernel pruning compresses CNNs better than existing compression methods.“The experimental results on the CIFAR-10/100 and ImageNet datasets show that compared to the existing CNN model compression methods, the IMSI-based progressive kernel pruning method exhibits better compression performance in processing the model compression tasks that are currently popular.”
   3. UncheckedPruning VGG-16 on CIFAR-10 with the authors' method gives 80.8x compression and 14.8x speed-up, with accuracy down only 0.59% from baseline.“In particular, pruning VGG-16 on CIFAR-10 with our model achieves a compression ratio of 80.8x and an acceleration ratio of 14.8×, which are 5.8× and 4.2× higher than the best results at present, respectively, and the classification accuracy decreases by only 0.59% relative to that of the baseline.”