Differentiable Channel Pruning Search

In this paper, we propose the differentiable channel pruning search (DCPS) of convolutional neural networks. Unlike traditional channel pruning algorithms which require users to manually set prune ratio for each convolutional layer, DCPS search the optimal combination of prune ratio that automatically. Inspired by the differentiable architecture search (DARTS), we draws lessons from the continuous relaxation and leverages the gradient information to balance the metrics and performance. However, directly applying the DARTS scheme will cause channel mismatching problem and huge memory consumption. Therefore, we introduce a novel weight sharing technique which can elegantly eliminate the shape mismatching problem with negligible additional resource. We test the proposed algorithm on image classification task and it achieves the state-of-the-art pruning results for image classification on CIFAR-10, CIFAR-100 and ImageNet. DCPS is further utilized for semantic segmentation on PASCAL VOC 2012 for two purposes. The first is to demonstrate that task-specific channel pruning achieves better performance against transferring slim models, and the second is to prove the memory efficiency of DCPS as the task demand more memory budget than classification. Results of the experiments validate the effectiveness and wide applicability of DCPS.