CurML: A Curriculum Machine Learning Library

ABSTRACTCurriculum learning (CL) is a machine learning paradigm gradually learning from easy to hard, which is inspired by human curricula. As an easy-to-use and general training strategy, CL has been widely applied to various multimedia tasks covering images, texts, audios, videos, etc. The effectiveness of CL has recently facilitated an increasing number of new CL algorithms. However, there has been no open-source library for curriculum learning, making it hard to reproduce, evaluate and compare the numerous CL algorithms on fair benchmarks and settings. To ease and promote future research on CL, we develop CurML, the first Curriculum Machine L earning library to integrate existing CL algorithms into a unified framework. It is convenient to use and flexible to customize by calling the provided five APIs, which are designed for easily plugging into a general training process and conducting the data-oriented, model-oriented and loss-oriented curricula. Furthermore, we present empirical results obtained by CurML to demonstrate the advantages of our library. The code is available online at https://github.com/THUMNLab/CurML.