CAPTOR: A Class Adaptive Filter Pruning Framework for Convolutional Neural Networks in Mobile Applications

Nowadays, the evolution of deep learning and cloud service significantly promotes neural network based mobile applications. Although intelligent and prolific, those applications still lack certain flexibility: For classification tasks, neural networks are generally trained online with vast classification targets to cover various utilization contexts. However, only partial classes are practically tested due to individual mobile user preference and application specificity. Thus the unneeded classes cause considerable computation and communication cost. In this work, we propose CAPTOR - a class-level reconfiguration framework for Convolutional Neural Networks (CNNs). By identifying the class activation preference of convolutional filters through feature interest visualization and gradient analysis, CAPTOR can effectively cluster and adaptively prune the filters associated with unneeded classes. Therefore, CAPTOR enables class-level CNN reconfiguration for network model compression and local deployment on mobile devices. Experiment shows that, CAPTOR can reduce computation load for VGG-16 by up to 40.5% and 37.9% energy consumption with ignored loss of accuracy. For AlexNet, CAPTOR also reduces computation load by up to 42.8% and 37.6% energy consumption with less than 3% loss in accuracy.