Deep super-class learning for long-tail distributed image classification

Highlights：

• We propose a deep super-class learning model for long-tail distribution classification. A block-structured sparse regularization term is designed and attached to the objective function. Thus, the deep model can obtain the super-class structure while learning the features and the classifier in an end-to-end procedure.

• The weight matrix of the classification layer learnt by the proposed model indicates the different importance evaluations on the learnt representation, which implies the cluster structure of the original classes.

• We present the performance evaluation of the proposed model on two real-world image datasets. The experimental results demonstrate that the super-class construction strategy can achieve better results for long-tail distribution classification, and the proposed model can further improve the performance of other relevant methods.