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InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity
[article]
2018
arXiv
pre-print
We demonstrate an approach to face attribute detection that retains or improves attribute detection accuracy across gender and race subgroups by learning demographic information prior to learning the attribute detection task. The system, which we call InclusiveFaceNet, detects face attributes by transferring race and gender representations learned from a held-out dataset of public race and gender identities. Leveraging learned demographic representations while withholding demographic inference
arXiv:1712.00193v3
fatcat:h5oq2baa4rbhliccezomis7cxi