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Gradually channel pruning algorithm via joint training

Convolutional neural network pruning is commonly adopted for alleviating the high computational cost of resource-limited devices. However, most pruning algorithms separate the pruning process from the fine-tuning process, so the whole pruning algorithm will take a long time. In this paper, we propose a joint training channel pruning algorithm (JTPA) that automatically prunes the network in channel level based on the stage stability in model training. The main idea is two-fold: a) through a priori experiments, it is proved that the training of convolutional neural network has the characteristics of phased fitting, and b) the importance of model channel is judged according to the stability of feature maps similarity and gradient value of stage. More specifically, we can judge whether this stage is well trained from the shape similarity stability of the stage feature map, so as to judge which channels are more important with the gradient value. In the experiments, ResNet series networks is selected to evaluate the pruning performance on CIFAR-10, CIFAR-100. The results demonstrate that compared with state-of-the-art of neural network pruning algorithms, the proposed JTPA model achieves not only has high pruning efficiency but also a better accuracy.