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Peer review (iclr2020):
The paper investigates the use of the information bottleneck principle in convolutional neural networks. It uses the principle to prune the filters of the convolutional network that shares the least mutual information with the label. The paper has both technical shortcomings as well as shortcomings in presentation. Technical shortcomings: The way mutual information is calculated in this work, by binning the filter outputs, is an approximation that should be further justified. It ignores potential correlations between the filters as well as the fact that the network uses continuous values. This approximation lacks technical justification. Presentation shortcomings: The paper does not do a good job at communicating its ideas. The introduction is very difficult to read and it does not give an insight into the content of the paper. Section 3 does not explain the notation and it does not clearly communicate the method used. Perhaps providing pseudocode for the algorithm would be helpful. Significance: The method performs worse than most recent approaches to pruning neural networks. (eg. Deep compression, Bayesian compression)