1.The authors have done a good job with placing their work appropriately. One point of weakness is insufficient comparison to approaches that aim to reduce spatial redudancy, or make the networks more efficient specifically the ones skipping layers/channels. Comparison to OctConv and SkipNet even for a single datapoint with say the same backbone architecture will be valuable to the readers. 2. The authors need to show a graph showing the plot of T vs number of images, and Expectation(T) over the imagenet test set. It is important to understand whether the performance improvement stems solely from the network design to exploit spatial redundancies, or whether the redudancies stem from the nature of ImageNet, ie., large fraction of images can be done with Glance and hence any algorithm with lower resolution will have an unfair advantage. Note, algorithms skipping layers or channels do not enjoy this luxury. 3. The authors should add results from [57] and discuss the comparison. Recent alternatives to MSDNets should be compared and discussed. 4. Efficient backbone architectures and approaches tailoring the computation by controlling convolutional operator have the added advantage that they can be generally applied to semantic (object recognition) and dense pixel-wise tasks. Extension of this approach, unlike other approaches exploiting spatial redundancy to alternate vision tasks is not straightforward. The authors should discuss the implications of this approach to other vision tasks.
2. The authors need to show a graph showing the plot of T vs number of images, and Expectation(T) over the imagenet test set. It is important to understand whether the performance improvement stems solely from the network design to exploit spatial redundancies, or whether the redudancies stem from the nature of ImageNet, ie., large fraction of images can be done with Glance and hence any algorithm with lower resolution will have an unfair advantage. Note, algorithms skipping layers or channels do not enjoy this luxury.