Learning deep face representation with long tail data: An aggregate and disperse approach
Abstract In this work, we study the problem of deep representation learning on a large face dataset with long tail distribution. Training convolutional neural networks on such dataset with conventional strategy suffers from imbalance problem which results in biased classification boundary, and the few shot classes lying in tail parts further make the model prone to overfitting. Aiming to learn more discriminative CNN model from long tail data, we propose a novel aggregate and disperse training schema. Firstly, our proposed method aggregates similar classes in tail part to avoid imbalance problem. Based on the aggregated super classes and those original head classes, a model is pre trained to capture accurate discrimination in head classes as well as coarse discrinimation in tail classes. Secondly, we selectively disperses those aggregated super classes to learn precise inter class variations and refine the representation for better generalization. We perform extensive experiments on MS Celeb 1M, BLUFR and MegaFace. Compared with baselines and existing methods, our method achieves better performance of face recognition, demonstrating its effectiveness of handling long tail distribution.
Pattern Recognit. Lett.