计算机工程与应用2024，Vol.60Issue(8)：1-15,15.DOI:10.3778/j.issn.1002-8331.2308-0271
基于深度元学习的小样本图像分类研究综述
Survey of Few-Shot Image Classification Based on Deep Meta-Learning
摘要
Abstract
Deep meta-learning has emerged as a popular paradigm for addressing few-shot classification problems.A comprehensive review of recent advancements in few-shot image classification algorithms based on deep meta-learning is provided.Starting from the problem description,the categorizes of the algorithms based on deep meta-learning for few-shot image classification are summarized,and commonly used few-shot image classification datasets and evaluation crite-ria are introduced.Subsequently,typical models and the latest research progress are elaborated in three aspects:model-based deep meta-learning methods,optimization-based deep meta-learning methods,and metric-based deep meta-learning methods.Finally,the performance analysis of existing algorithms on popular public datasets is presented,the research hotspots in this topic are summarized,and its future research directions are discussed.关键词
深度学习/元学习/小样本学习/图像分类Key words
deep learning/meta learning/few-shot learning/image classification分类
信息技术与安全科学引用本文复制引用
周伯俊,陈峙宇..基于深度元学习的小样本图像分类研究综述[J].计算机工程与应用,2024,60(8):1-15,15.基金项目
国家自然科学基金面上项目(61973178) （61973178）
江苏省重点研发计划(BE2021063). （BE2021063）
