A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks

Author:

Yan Sifan1ORCID,Zhang Yaotian1,Gao Fei1ORCID,Sun Jinping1ORCID,Hussain Amir2,Zhou Huiyu3ORCID

Affiliation:

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

2. Cyber and Big Data Research Laboratory, Edinburgh Napier University, Edinburgh, U.K.

3. Department of Informatics, University of Leicester, Leicester, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Reference52 articles.

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3. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;tarvainen;Proc Adv Neural Inf Process Syst,0

4. Temporal ensembling for semi-supervised learning;laine;Proc Int Conf Learn Representations,0

5. Regularization with stochastic transformations and perturbations for deep semi-supervised learning;sajjadi;Proc Int Conf Neural Inf Process,0

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