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Soft Masking for Cost-Constrained Channel Pruning. (arXiv:2211.02206v1 [cs.CV])
Nov. 7, 2022, 2:14 a.m. | Ryan Humble, Maying Shen, Jorge Albericio Latorre, Eric Darve1, Jose M. Alvarez
cs.CV updates on arXiv.org arxiv.org
Structured channel pruning has been shown to significantly accelerate
inference time for convolution neural networks (CNNs) on modern hardware, with
a relatively minor loss of network accuracy. Recent works permanently zero
these channels during training, which we observe to significantly hamper final
accuracy, particularly as the fraction of the network being pruned increases.
We propose Soft Masking for cost-constrained Channel Pruning (SMCP) to allow
pruned channels to adaptively return to the network while simultaneously
pruning towards a target cost constraint. …
More from arxiv.org / cs.CV updates on arXiv.org
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