Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation-Reference-Cited by-同舟云学术

Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation

Author:

Liu Chao¹^ORCID,Fu Xue¹^ORCID,Wang Yu¹^ORCID,Guo Lantu²,Liu Yuchao³,Lin Yun⁴^ORCID,Zhao Haitao¹^ORCID,Gui Guan¹^ORCID

Affiliation:

1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

2. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

3. School of Electronic Information Engineering, Beihang University, Beijing, China

4. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Funder

National Natural Science Foundation of China

Key Project of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province

Postgraduate Graduate Research and Practice Innovation Programs 2023

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference58 articles.

1. Un-mix: Rethinking image mixtures for unsupervised visual representation learning;shen;arXiv 2003 05438,2020

3. A simple framework for contrastive learning of visual representations;chen;Proc Int Conf Mach Learn (ICML),2020

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