Web-scale training for face identification

@article{Taigman2015WebscaleTF,
  title={Web-scale training for face identification},
  author={Yaniv Taigman and Ming Yang and Marc'Aurelio Ranzato and Lior Wolf},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={2746-2754}
}
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… CONTINUE READING
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