Reviews: Doubly Convolutional Neural Networks

Added after rebuttal / discussion: Although I still contend that the CIFAR-10 baseline network used in the paper is unnecessarily weak, I appreciate that results on a large-scale dataset such as ImageNet (on which the method is shown to be quite competitive) are much more relevant, and I no longer consider it a major issue. I still disagree with the presentation of the idea (asserting that the filters are "translated versions of each other", when the reason the method works is precisely because they are _not_ exact translated versions of each other, only approximately so), but I guess that can be put down to a matter of taste. That leaves the problem with the CyclicCNN baseline, which I maintain is unnecessarily crippled by removing its ability to use relative orientation information. My issue was not with the fact that this is not explained in enough detail (as the rebuttal seems to imply), but rather that the model is wrong. This form of parameter sharing is pointless, except in very rare cases where relative orientation information is not relevant to the task at hand (I can't think of any situations where this is true, but there might be some).