  Augmenting data in image space (eg. flipping, cropping etc) and activation
space (eg. dropout) are being widely used to regularise deep neural networks
and have been successfully applied on several computer vision tasks. Unlike
previous works, which are mostly focused on doing augmentation in the
aforementioned domains, we propose to do augmentation in label space. In this
paper, we present a novel method to generate fixed dimensional labels with
continuous values for images by exploiting the word2vec representations of the
existing categorical labels. We then append these representations with existing
categorical labels and train the model. We validated our idea on two
challenging face attribute classification data sets viz. CelebA and LFWA. Our
extensive experiments show that the augmented labels improve the performance of
the competitive deep learning baseline and reduce the need of annotated real
data up to 50%, while attaining a performance similar to the state-of-the-art
methods.
