Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks
This work addresses model compression for mobile deployment, offering a novel structured pruning approach that is synergistic with other methods, though it is incremental in the context of existing network pruning techniques.
The paper tackles the problem of high computational cost in convolutional neural networks for mobile devices by introducing a block-wise pruning method with auxiliary gating structures, achieving over 93% FLOPs reduction while maintaining or improving performance compared to unpruned models.
Convolutional neural networks are prevailing in deep learning tasks. However, they suffer from massive cost issues when working on mobile devices. Network pruning is an effective method of model compression to handle such problems. This paper presents a novel structured network pruning method with auxiliary gating structures which assigns importance marks to blocks in backbone network as a criterion when pruning. Block-wise pruning is then realized by proposed voting strategy, which is different from prevailing methods who prune a model in small granularity like channel-wise. We further develop a three-stage training scheduling for the proposed architecture incorporating knowledge distillation for better performance. Our experiments demonstrate that our method can achieve state-of-the-arts compression performance for the classification tasks. In addition, our approach can integrate synergistically with other pruning methods by providing pretrained models, thus achieving a better performance than the unpruned model with over 93\% FLOPs reduced.