Reviews: Frequency-Domain Dynamic Pruning for Convolutional Neural Networks

My only major issue has been addressed and the same is true for my minor questions and issues, except for (5), which I do not consider crucial, particularly given that the authors only have one page for their response. Since most of my issues were regarding question that I had or minor detail that should be added to the paper, I have raised my confidence of reproducibility to 3. ] The paper introduces a novel method for parameter-pruning in convolutional neural networks that operates in the frequency domain. The latter is a natural domain to determine parameter-importance for convolutional filters – most filters of a trained neural network are smooth and thus have high energy (i.e. An additional advantage of the method is that pruning is not performed as a single post-training step, but parameters can be pruned and re-introduced during training in a continuous fashion, which has been shown to be beneficial in previous pruning schemes. The method is evaluated on three different image classification tasks (with a separate network architecture each) and outperforms the methods it is compared against.