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

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.

arXiv comment7 pages

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Cited by in corpus (2)

• A Comparative Review of Recent Few-Shot Object Detection Algorithms
• One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction