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We propose a simple but effective data-driven channel pruning algorithm,which compresses deep neural networks in a differentiable way by exploiting thecharacteristics of operations. The proposed approach makes a jointconsideration of batch normalization (BN) and rectified linear unit (ReLU) forchannel pruning; it estimates how likely the two successive operationsdeactivate each feature map and prunes the channels with high probabilities. Tothis end, we learn differentiable masks for individual channels and make softdecisions throughout the optimization procedure, which facilitates to explorelarger search space and train more stable networks. The proposed frameworkenables us to identify compressed models via a joint learning of modelparameters and channel pruning without an extra procedure of fine-tuning. Weperform extensive experiments and achieve outstanding performance in terms ofthe accuracy of output networks given the same amount of resources whencompared with the state-of-the-art methods.

Operation-Aware Soft Channel Pruning using Differentiable Masks