1. The novelty is a little weak. It is not clear what's the significant difference and advantage compared to NCA [6] and "Small codes and large image databases for recognition", A. Torralba et al., 2008, which used NCA in deep learning. 2. In the experiment of face recognition, some state-of-the art references are missing, such as Baidu' work "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding", http://vis-www.cs.umass.edu/lfw/results.html#baidu. In that work, the triplet loss is also used and it reported the result trained on the dataset containing 9K identities and 450K images, which is similar with Webface. The VRF can achieve 98.65% on LFW which is better than the result in Table 3 in this paper.
2. In the experiment of face recognition, some state-of-the art references are missing, such as Baidu' work "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding", http://vis-www.cs.umass.edu/lfw/results.html#baidu. In that work, the triplet loss is also used and it reported the result trained on the dataset containing 9K identities and 450K images, which is similar with Webface. The VRF can achieve 98.65% on LFW which is better than the result in Table 3 in this paper.