Reviews: Learning Versatile Filters for Efficient Convolutional Neural Networks

The paper introduces two new types of convolutional filters, named versatile filters, which can reduce the memory and FLOP requirements of conv. The method is simple and, according to the experimental results, it seems to be effective. The text quality is OK, although it would definitively benefit from better explanations about the proposed method. For instance, Figure 1 is a bit confusing (Are you showing 4 different filters in Fig 1 (b)?). My main concerns about this paper are related to the experiments and results, as detailed in the following questions: (1) Regarding the FLOP reduction, it is not clear how the reduction in the number of computations is really achieved.