Self-paced learning with privileged information

Wei Xu, Wei Liu, Haoyuan Chi, Song Qiu, Yu Jin

Abstract: Self-paced learning (SPL) is a powerful learning framework, where data from easy ones to more complex ones are gradually involved in the learning process. However, SPL is unable to exploit prior knowledge, so it is prone to overfitting. To alleviate this problem, we propose a framework called self-paced learning with privileged information (SPL+), where privileged information is introduced as prior knowledge to guide the curriculum learned by SPL. Specifically, the learning process using weighted privileged information and the curriculum learning process guided by privileged information are iteratively performed until the final mature curriculum guided by privileged information is learned. As this curriculum learning process can gradually grasp the easy to hard knowledge under the guidance of the robust high level privileged information, a more reliable model can be learned. Moreover, our SPL+ is a generalized framework, which is applicable to various problems. Comprehensive experiments demonstrate that our SPL+ outperforms the conventional SPL based method for three applications including action recognition, scene recognition and handwritten digit recognition.