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ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting
[article]
2021
arXiv
pre-print
We propose ResRep, a novel method for lossless channel pruning (a.k.a. filter pruning), which slims down a CNN by reducing the width (number of output channels) of convolutional layers. Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into the remembering parts and forgetting parts, where the former learn to maintain the performance and the latter learn to prune. Via training with regular SGD on the former but a
arXiv:2007.03260v4
fatcat:6pyaguorqfgexmvttzzp6wxhre