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- 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
- Never Ending Language Learner (NELL) (Carlson, Betteridge, and Kisiel 2010)
Bibliography
Carlson, Andrew, Justin Betteridge, and Bryan Kisiel. 2010. “Toward an Architecture for Never-Ending Language Learning.”. In Proceedings of the Conference on Artificial Intelligence (AAAI) (2010), 1306–13.
De Lange, Matthias, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. May 2020. “A Continual Learning Survey: Defying Forgetting in Classification Tasks”. arXiv:1909.08383 [Cs, Stat], May.