Task: Language Identification
Input (Spanish, English): The paper presents an interesting incremental approach for exploring new convolutional network hierarchies in an incremental manner after a baseline network has reached a good recognition performance.  The experiments are presented for the CIFAR-100 and ImageNet benchmarks by morphing various ResNet models into better performing models with somewhat more computation.  Although the baselines are less strong than those presented in the literature, the paper claims significant error reduction for both ImageNet and CIFAR-100.  The main idea of the paper is to rewrite convolutions into multiple convolutions while expanding the number of filters. It is quite unexpected that this approach yields any improvements over the baseline model at all.  However, for some of the basic tenets of network morphing, experimental evidence is not given in the paper. Here are some fundamental questions raised by the paper: - How does the quality of morphed networks compares to those with the same topology trained from scratch? - How does the incremental training time after morphing relate to that of the network trained from scratch? - Where is the extra computational cost of the morphed networks come from? - Why is the quality of the baseline ResNet models lag behind those that are reported in the literature and github? (E.g. the github ResNet-101 model is supposed to have 6.1% top-5 recall vs 6.6 reported in the paper) More evidence for the first three points would be necessary to evaluate the validity of the claims of the paper.  The paper is written reasonably well and can be understood quite well, but the missing evidence and weaker baselines make it looks somewhat less convincing.  I would be inclined to revise up the score if a more experimental evidence were given for the main message of the paper (see the points above).
Output (English): en