Continual learning

Machine learning

Continual learning is a type of supervised learning where there is no “testing phase” associated to a decision process. Instead, training samples keep being processed by the algorithm which has to simultaneously make predictions and keep learning.

A definition from the survey (De Lange et al. 2020):

The General Continual Learning setting considers an infinite stream of training data where at each time step, the system receives a (number of) new sample(s) drawn non i.i.d from a current distribution that could itself experience sudden of gradual changes.

Examples of continual learning systems


. . “Toward an Architecture for Never-Ending Language Learning.”. In Proceedings of the Conference on Artificial Intelligence (AAAI) (2010), 1306–13.

. . “A Continual Learning Survey: Defying Forgetting in Classification Tasks”. arXiv:1909.08383 [Cs, Stat], May.

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