  Scaling machine learning methods to very large datasets has attracted
considerable attention in recent years, thanks to easy access to ubiquitous
sensing and data from the web. We study face recognition and show that three
distinct properties have surprising effects on the transferability of deep
convolutional networks (CNN): (1) The bottleneck of the network serves as an
important transfer learning regularizer, and (2) in contrast to the common
wisdom, performance saturation may exist in CNN's (as the number of training
samples grows); we propose a solution for alleviating this by replacing the
naive random subsampling of the training set with a bootstrapping process.
Moreover, (3) we find a link between the representation norm and the ability to
discriminate in a target domain, which sheds lights on how such networks
represent faces. Based on these discoveries, we are able to improve face
recognition accuracy on the widely used LFW benchmark, both in the verification
(1:1) and identification (1:N) protocols, and directly compare, for the first
time, with the state of the art Commercially-Off-The-Shelf system and show a
sizable leap in performance.
