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.

在这项工作中，我们研究具有长尾分布的大型人脸数据集上的深度表示学习问题。使用常规策略在这种数据集上训练卷积神经网络会遇到不平衡问题，这会导致分类边界出现偏差，而位于尾部的少量镜头类进一步使模型易于过拟合。为了从长尾数据中学习更多的判别式CNN模型，我们提出了一种新颖的汇总分散训练模式。首先，我们提出的方法在尾部聚合相似的类，以避免不平衡问题。基于聚集的超类和原始头类，对模型进行预训练，以捕获头类中的准确区分以及尾类中的粗判别。其次，我们有选择地分散那些聚集的超类，以学习精确的类间差异，并完善表示形式以实现更好的泛化。我们在MS-Celeb-1M，BLUFR和MegaFace上进行了广泛的实验。与基线和现有方法相比，我们的方法获得了更好的人脸识别性能，证明了其在处理长尾分布方面的有效性。