基于深度神经网络的少样本学习综述

如何从少数训练样本中学习并识别新的类别对于深度神经网络来说是一个具有挑战性的问题。针对如何解决少样本学习的问题，全面总结了现有的基于深度神经网络的少样本学习方法，涵盖了方法所用模型、数据集及评估结果等各个方面。具体地，针对基于深度神经网络的少样本学习方法，提出将其分为数据增强方法、迁移学习方法、度量学习方法和元学习方法四种类别；对于每个类别，进一步将其分为几个子类别，并且在每个类别与方法之间进行一系列比较，以显示各种方法的优劣和各自的特点。最后强调了现有方法的局限性，并指出了少样本学习研究领域未来的研究方向。

Survey of few-shot learning based on deep neural network

How to learn and identify new categories from a small number of training samples is a challenging problem for deep neural networks. For how to solve the problem of few-shot learning, this paper comprehensively summarized the existing few-shot learning methods based on deep neural networks, which covered various aspects such as models used in the methods, datasets and evaluation results. Specifically, for the few-shot learning method based on deep neural network, this paper divided it into four categories, namely data enhancement method, migration learning method, metric learning method and meta-learning method. For each category, this paper further divided it into sub-categories and conducted a series of comparisons between each category and method to show the pros and cons of the various methods and their respective characteristics. Finally, the paper highlighted the limitations of existing methods and pointed to future research directions in the field of few-sfhot learning.