Deep Model Compression based on the Training History. (arXiv:2102.00160v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2102.00160

Deep Convolutional Neural Networks (DCNNs) have shown promising performances
in several visual recognition problems which motivated the researchers to
propose popular architectures such as LeNet, AlexNet, VGGNet, ResNet, and many
more. These architectures come at a cost of high computational complexity and
parameter storage. To get rid of storage and computational complexity, deep
model compression methods have been evolved. We propose a "History Based Filter
Pruning (HBFP)" method that utilizes network training history for filter
pruning. Specifically, we prune the …

arxiv compression cv deep history model on training