### Survey of Few Shot Learning of Deep Neural Network

ZHU Juntao, YAO Guangle, ZHANG Gexiang, LI Jun, YANG Qiang, WANG Sheng, YE Shaoze

1. 1.School of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, China
2.Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu 610059, China
3.School of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
4.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
5.Shenzhen Investigation and Research Institute Co. , Ltd., Shenzhen, Guangdong 518026, China
• Online:2021-04-01 Published:2021-04-02

### 深度神经网络的小样本学习综述

1. 1.成都理工大学 信息科学与技术学院，成都 610059
2.成都理工大学 人工智能研究中心，成都 610059
3.成都理工大学 环境与土木工程学院，成都 610059
4.成都理工大学 地质灾害防治与地质环境保护国家重点实验室，成都 610059
5.深圳市勘察研究院有限公司，广东 深圳 518026

Abstract:

With the recent vigorous development of deep learning, Deep Neural Networks（DNN） have made exciting breakthrough in large-scale image classification and recognition tasks, but they still face huge challenges in solving few shot learning problems. Few Shot Learning（FSL） is defined as learning a model that can solve practical problems with a small number of supervised samples, which is of great significance in the field of deep learning. This prompts people to systematically combs the recent work of few shot learning of DNN, and divide the solution into four strategies according to the technology they used to solve the small sample learning problem：data augmentation, metric learning, external memory, parameter optimization. According to these strategies, it comprehensively reviews the existing few shot learning methods of DNN, and summarizes the performance of each strategy on relevant benchmarks. Finally, the limitations of the existing technology are emphasized and its future development direction is prospected to provide reference for future research work.