A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics

Abstract

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The ‘network layer performance evaluation metrics’ are obtained from the number of pixel activations in the heat map. The network layer with the lowest ‘network layer performance evaluation metrics’ is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet.

A Novel Deep-Learning Model Compression Based on Filter-Stripe Group Pruning and Its IoT Application

“…However, model deployment is sometimes costly due to the large number of parameters in the DNN. To address this problem, many methods [ 1 , 2 , 3 , 4 , 5 , 6 ] have been proposed to compress networks and reduce computational quantities. These methods are mainly divided into two categories: structured pruning and unstructured pruning, in which the main method of structured pruning is the filter pruning, while the unstructured pruning method is mainly achieved by weight pruning.…”