Continual Meta-Learning combines two important learning paradigms: Continual Learning and Meta-Learning.
Continual Learning enables a model to learn a sequence of tasks one after another without forgetting previous tasks (avoiding catastrophic forgetting).
Meta-Learning focuses on training models so that they can quickly adapt to new tasks with few examples.
In Continual Meta-Learning, a model is trained to continuously master new tasks while maintaining its ability for rapid adaptation.
Typical techniques include memory mechanisms, regularizations, and fast adaptation strategies to preserve knowledge and remain flexible.