Reviews: Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

Especially in [47], a similar two-step framework was proposed, which does not have to train from scratch (Inaccurate description in Page 3 row 83-84). It would be great if authors could describe the new insights of using Taylor expansion to estimate the change of loss along with importance of a filter. It would be great if authors could compare with [26], [47] and ''Data-Driven Sparse Structure Selection for Deep Neural Networks'' (Huang and Wang, 2017) due to the similarity of these ideas. Given the results presented on ImageNet using ResNet-50, which is 0.31 accuracy improvement and 40% FLOPs reduction, it's difficult to compare with (Huang and Wang, 2017) with results of error 26.8 and 66% reduction.