  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 redundant filters by observing similar
patterns in the filter's L1-norms (absolute sum of weights) over the training
epochs. We iteratively prune the redundant filters of a CNN in three steps.
First, we train the model and select the filter pairs with redundant filters in
each pair. Next, we optimize the network to ensure an increased measure of
similarity between the filters in a pair. This optimization of the network
facilitates us to prune one filter from each pair based on its importance
without much information loss. Finally, we retrain the network to regain the
performance, which is dropped due to filter pruning. We test our approach on
popular architectures such as LeNet-5 on MNIST dataset; VGG-16, ResNet-56, and
ResNet-110 on CIFAR-10 dataset, and ResNet-50 on ImageNet. The proposed pruning
method outperforms the state-of-the-art in terms of FLOPs reduction
(floating-point operations) by 97.98%, 83.42%, 78.43%, 74.95%, and 75.45% for
LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50, respectively, while
maintaining the less error rate.
