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Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
[article]
2016
arXiv
pre-print
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely utilized for this problem. In this paper, however, we consider the mid-level CNN features as an
arXiv:1602.01827v3
fatcat:d633vqzcbfcs3ijnsiwimyyume