When Meta-Learning Meets Online and Continual Learning: A Survey
Jaehyeon Son Soochan Lee Gunhee Kim
Over the past decade, deep neural networks have demonstrated significant
success using the training scheme that involves mini-batch stochastic gradient
descent on extensive datasets. Expanding upon this accomplishment, there has
been a surge in research exploring the application of neural networks in other
learning scenarios. One notable framework that has garnered significant
attention is meta-learning. Often described as "learning to learn,"
meta-learning is a data-driven approach to optimize the learning algorithm.
Other branches of interest are continual learning and online learning, both of
which involve incrementally updating a model with streaming data. While these
frameworks were initially developed independently, recent works have started
investigating their combinations, proposing novel problem settings and learning
algorithms. However, due to the elevated complexity and lack of unified
terminology, discerning differences between the learning frameworks can be
challenging even for experienced researchers. To facilitate a clear
understanding, this paper provides a comprehensive survey that organizes
various problem settings using consistent terminology and formal descriptions.
By offering an overview of these learning paradigms, our work aims to foster
further advancements in this promising area of research.