Doing the Best We Can With What We Have: Multi-Label Balancing With Selective Learning for Attribute Prediction

Abstract

Attributes are human describable features, which have been used successfully for face, object, and activity recognition. Facial attributes are intuitive descriptions of faces and have proven to be very useful in face recognition and verification. Despite their usefulness, to date there is only one large-scale facial attribute dataset, CelebA. Impressive results have been achieved on this dataset, but it exhibits a variety of very significant biases. As CelebA contains mostly frontal idealized images of celebrities, it is difficult to generalize a model trained on this data for use on another dataset (of non celebrities). A typical approach to dealing with imbalanced data involves sampling the data in order to balance the positive and negative labels, however, with a multi-label problem this becomes a non-trivial task. By sampling to balance one label, we affect the distribution of other labels in the data. To address this problem, we introduce a novel Selective Learning method for deep networks which adaptively balances the data in each batch according to the desired distribution for each label. The bias in CelebA can be corrected for in this way, allowing the network to learn a more robust attribute model. We argue that without this multi-label balancing, the network cannot learn to accurately predict attributes that are poorly represented in CelebA. We demonstrate the effectiveness of our method on the problem of facial attribute prediction on CelebA, LFWA, and the new University of Maryland Attribute Evaluation Dataset (UMD-AED), outperforming the state-of-the-art on each dataset.