Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a chann...

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers