Few-Shot SAR Target Recognition Through Meta-Adaptive Hyperparameters Learning for Fast Adaptation-Reference-Cited by-同舟云学术

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Few-Shot SAR Target Recognition Through Meta-Adaptive Hyperparameters Learning for Fast Adaptation

Published:2023 Issue: Volume:61 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zeng Zhiqiang¹^ORCID,Sun Jinping¹^ORCID,Wang Yanping²^ORCID,Gu Dandan³,Han Zhu⁴^ORCID,Hong Wen⁴

Affiliation:

1. School of Electronic and Information Engineering, Beihang University, Beijing, China

2. School of Information Science and Technology, North China University of Technology, Beijing, China

3. National Key Laboratory of Scattering and Radiation, Shanghai Radio Equipment Research Institute, Shanghai, China

4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Defense Science and Technology Key Laboratory Foundation

Academic Excellence Foundation of Beihang University (BUAA) for Ph.D. Students

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10287966.pdf?arnumber=10287966

Reference45 articles.

1. Meta-Transfer Learning Through Hard Tasks

2. Few-Shot SAR-ATR Based on Instance-Aware Transformer

3. AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing

4. A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition

5. FlipDA: Effective and robust data augmentation for few-shot learning;zhou;arXiv 2108 06332,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition;Remote Sensing;2024-09-03

2. Multilevel Attention Networks for Synthetic Aperture Radar Automatic Target Recognition;IEEE Geoscience and Remote Sensing Letters;2024

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