Reviews: Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks

The authors report increased accuracy and robustness by adding an extremely simple, linear filtering with a difference-of-Gaussian kernel. While this would be a quite remarkable finding if it turned out to be reliable, I have substantial doubts, which I will outline below: - Instead of using a well-established baseline (e.g. a ResNet), the authors train their own architecture (similar to VGG) on what seems to be their own 100-class variant of ImageNet. Despite these simplifications, their performance is quite poor (a ResNet-18 would achieve 70% top-1 accuracy on full ImageNet). As a consequence, we don't really know whether the improvement by their surround modulation module is an artefact of a poorly trained model or a real effect. Training a ResNet on ImageNet is trivial these days (scripts are provided in the official PyTorch or Tensorflow repositories), so it's really unclear to me why the authors try to establish their own baseline.