Deep Model Compression based on the Training History

Deep Convolutional Neural Networks (DCNNs) have shown promising results 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 novel 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 L1-norms of filters (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 increase the similarity between the filters in a pair. It 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 and VGG-16, ResNet-56, and ResNet-110 on CIFAR-10 dataset. 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%, and 74.95% for LeNet-5, VGG-16, ResNet-56, and ResNet-110 models, respectively, while maintaining the less error rate.