Meta-learning approaches for few-shot learning: A survey of recent advances

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Gharoun, Hassan, Momenifar, Fereshteh, Chen, Fang, Gandomi, Amir H
Format: Article
Language:English
Subjects:
Deep learning
Multidimensional data
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (I) metric-based, (II) memory-based, (III), and learning-based methods. Finally, current challenges and insights for future researches are discussed.
ISSN:2331-8422