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Data for face analysis often exhibit highly-skewed class distribution, i.e.,most data belong to a few majority classes, while the minority classes onlycontain a scarce amount of instances. To mitigate this issue, contemporary deeplearning methods typically follow classic strategies such as class re-samplingor cost-sensitive training. In this paper, we conduct extensive and systematicexperiments to validate the effectiveness of these classic schemes forrepresentation learning on class-imbalanced data. We further demonstrate thatmore discriminative deep representation can be learned by enforcing a deepnetwork to maintain inter-cluster margins both within and between classes. Thistight constraint effectively reduces the class imbalance inherent in the localdata neighborhood, thus carving much more balanced class boundaries locally. Weshow that it is easy to deploy angular margins between the clusterdistributions on a hypersphere manifold. Such learned Cluster-based LargeMargin Local Embedding (CLMLE), when combined with a simple k-nearest clusteralgorithm, shows significant improvements in accuracy over existing methods onboth face recognition and face attribute prediction tasks that exhibitimbalanced class distribution.

Deep Imbalanced Learning for Face Recognition and Attribute Prediction