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Curriculum Learning: A Survey

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Abstract

Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs. Curriculum learning strategies have been successfully employed in all areas of machine learning, in a wide range of tasks. However, the necessity of finding a way to rank the samples from easy to hard, as well as the right pacing function for introducing more difficult data can limit the usage of the curriculum approaches. In this survey, we show how these limits have been tackled in the literature, and we present different curriculum learning instantiations for various tasks in machine learning. We construct a multi-perspective taxonomy of curriculum learning approaches by hand, considering various classification criteria. We further build a hierarchical tree of curriculum learning methods using an agglomerative clustering algorithm, linking the discovered clusters with our taxonomy. At the end, we provide some interesting directions for future work.

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Acknowledgements

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Authors and Affiliations

  1. Department of Computer Science, University of Bucharest, 010014, Bucharest, Romania

    Petru Soviany

  2. Department of Computer Science and Romanian Young Academy, University of Bucharest, 010014, Bucharest, Romania

    Radu Tudor Ionescu

  3. Department of Information Engineering and Computer Science, University of Trento, 38123, Povo-Trento, Italy

    Paolo Rota & Nicu Sebe

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This work was supported by a Grant of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, Project Number PN-III-P1-1.1-TE-2019-0235, within PNCDI III. This article has also benefited from the support of the Romanian Young Academy, which is funded by Stiftung Mercator and the Alexander von Humboldt Foundation for the period 2020–2022. This work was also supported by European Union’s Horizon 2020 research and innovation programme under Grant No. 951911 - AI4Media.

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Soviany, P., Ionescu, R.T., Rota, P. et al. Curriculum Learning: A Survey. Int J Comput Vis 130, 1526–1565 (2022). https://doi.org/10.1007/s11263-022-01611-x

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