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Progressive Channel-Shrinking Network
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AI
DOI：10.1109/TMM.2023.3291197.png)
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摘要
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Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience to guide pruning. Therefore, salience-based channel pruning can dynamically adjust the channel width at run-time, which provides a flexible pruning scheme. However, there are two problems emerging: a gating function is often needed to truncate the specific salience entries to zero, which destabilizes the forward propagation; dynamic architecture brings more cost for indexing in inference which bottlenecks the inference speed. In this article, we propose a Progressive Channel-Shrinking (PCS) method to compress the selected salience entries at run-time instead of roughly approximating them to zero. We also propose a Running Shrinking Policy to provide a testing-static pruning scheme that can reduce the memory access cost for filter indexing. We evaluate our method on ImageNet and CIFAR10 datasets over two prevalent networks: ResNet and VGG, and demonstrate that our PCS outperforms all baselines and achieves state-of-the-art in terms of compression-performance tradeoff. Moreover, we observe a significant and practical acceleration of inference. The code is available at https://github.com/JianhongPan-VLG/Progressive.Channel-Shrinking.Network.
Keyword:
Training
Indexing
Convolution
Costs
Generators
Feature extraction
Testing
Progressive
network shrinking
期刊
IF:
9.7
论文数:
4.6K
被引数:
2.4W
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