- 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)
- 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.