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

•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.