2021

Curriculum Learning: A Survey

Soviany, Petru, Ionescu, Radu Tudor, Rota, Paolo et al.

Understand

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.

Built on

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Similar

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