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1. Introduction Meta-learning, often termed as “learning to learn”, is a potent technique that facilitates rapid learning from limited data [1,2,3]. This approach encompasses learning from a collection of sample tasks, with each task representing a distinct learning problem. The primary objective is to glean knowledge from these tasks, thereby expediting the learning process for new tasks, even with minimal samples.
Continual learning is an essential building block of real-time learning [1], [2], [3], [4] and artificial general intelligence [5]. Unlike the conventional batch learning (also referred to as offline learning in some contexts [6]), wherein neural networks are trained exclusively during the training phase and remain unaltered during the test time, continual learning involves the incremental training of a non-independent and identically distributed (Non-IID) sequence of tasks.
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