Review for NeurIPS paper: SCOP: Scientific Control for Reliable Neural Network Pruning

This paper presents a method to prune filters in convolutional neural networks by introducing a scientific control group of knockoff features to reduce the disturbance of irrelevant factors. The authors also analyze the knockoff condition theoretically and derive the knockoff features given the knockoff data. Experiments are performed on CIFAR-10 and ImageNet. The reviewers and AC have read the author feedback carefully in addition to all the reviews. It is generally agreed that the proposed method is novel and interesting in that there is no need to specify arbitrary thresholds and hyperparameters for pruning.