Predicting face attributes in the wild is challenging due
to complex face variations. We propose a novel deep
learning framework for attribute prediction in the wild.
It cascades two CNNs, LNet and ANet, which are fine-
tuned jointly with attribute tags, but pre-trained differently.
LNet is pre-trained by massive general object categories for
face localization, while ANet is pre-trained by massive face
identities for attribute prediction. This framework not only
outperforms the state-of-the-art with a large margin, but
also reveals valuable facts on learning face representation.
