Meta self-paced learning

Abstract

Self-paced learning (SPL) is a learning regime, inspired by human and animal learning processes, that gradually incorporates simple to more complex samples into a training dataset. Recently, SPL has seen significant research progress. However, current SPL algorithms still have critical limitations, such as how to determine the age hyper-parameters (especially the age parameters). Some heuristic strategies based on cross-validation have been designed. In addition, setting these parameters manually has been proposed. However, such strategies are very inefficient, not supported by theoretical evidence, and are very difficult to apply generally in practice. To address these issues, we propose a meta-learning regime for adaptively learning age parameters involved in SPL. Three types of typical SPL algorithms are integrated into the proposed regime, and their accuracy and generalization capability are substantiated through regression and classification experiments, and compared to conventional SPL paradigms that do not include adaptive age parameter tuning.