A Concise Review of Recent Few-shot Meta-learning Methods

TL;DR

This paper provides a concise review of recent few-shot meta-learning methods, categorizing them into four groups, and discusses current challenges and future directions in the field.

Contribution

It offers a structured overview of recent advances in few-shot meta-learning, highlighting key methods and identifying open challenges.

Findings

Categorized methods into four technical groups

Identified key challenges in current few-shot meta-learning

Outlined future research directions

Abstract

Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning.