  Meta-learning empowers learning systems with the ability to acquire knowledge
from multiple tasks, enabling faster adaptation and generalization to new
tasks. This review provides a comprehensive technical overview of
meta-learning, emphasizing its importance in real-world applications where data
may be scarce or expensive to obtain. The paper covers the state-of-the-art
meta-learning approaches and explores the relationship between meta-learning
and multi-task learning, transfer learning, domain adaptation and
generalization, self-supervised learning, personalized federated learning, and
continual learning. By highlighting the synergies between these topics and the
field of meta-learning, the paper demonstrates how advancements in one area can
benefit the field as a whole, while avoiding unnecessary duplication of
efforts. Additionally, the paper delves into advanced meta-learning topics such
as learning from complex multi-modal task distributions, unsupervised
meta-learning, learning to efficiently adapt to data distribution shifts, and
continual meta-learning. Lastly, the paper highlights open problems and
challenges for future research in the field. By synthesizing the latest
research developments, this paper provides a thorough understanding of
meta-learning and its potential impact on various machine learning
applications. We believe that this technical overview will contribute to the
advancement of meta-learning and its practical implications in addressing
real-world problems.
