It is not uncommon that real-world data are distributed with a long tail. For
such data, the learning of deep neural networks becomes challenging because it
is hard to classify tail classes correctly. In the literature, several existing
methods have addressed this problem by reducing <
本文研究如何学习 deep learning 中的 feature 并解决长尾数据集中头尾类别分布错位、影响特征判别能力的问题。我们提出了使用‘特征云’方法来恢复长尾数据集的‘类内多样性’，并在 person re-identification 和 face recognition 任务中进行实验验证。