Abstract
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.
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