Dynamic Curriculum Learning for Imbalanced Data Classification

Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, Junjie Yan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 5017-5026


Human attribute analysis is a challenging task in the field of computer vision. One of the significant difficulties is brought from largely imbalance-distributed data. Conventional techniques such as re-sampling and cost-sensitive learning require prior-knowledge to train the system. To address this problem, we propose a unified framework called Dynamic Curriculum Learning (DCL) to adaptively adjust the sampling strategy and loss weight in each batch, which results in better ability of generalization and discrimination. Inspired by curriculum learning, DCL consists of two-level curriculum schedulers: (1) sampling scheduler which manages the data distribution not only from imbalance to balance but also from easy to hard; (2) loss scheduler which controls the learning importance between classification and metric learning loss. With these two schedulers, we achieve state-of-the-art performance on the widely used face attribute dataset CelebA and pedestrian attribute dataset RAP.

Related Material

author = {Wang, Yiru and Gan, Weihao and Yang, Jie and Wu, Wei and Yan, Junjie},
title = {Dynamic Curriculum Learning for Imbalanced Data Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}