基于深度元学习的小样本图像分类研究综述OA北大核心CSTPCD

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.