Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot...
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification
About this item
Full title
Author / Creator
Li, Yong , Shao, Zhenfeng , Huang, Xiao , Cai, Bowen and Peng, Song
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
The performance of deep learning is heavily influenced by the size of the learning samples, whose labeling process is time consuming and laborious. Deep learning algorithms typically assume that the training and prediction data are independent and uniformly distributed, which is rarely the case given the attributes and properties of different data...
Alternative Titles
Full title
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_542b9e5cfeed409b92d322af748df1af
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_542b9e5cfeed409b92d322af748df1af
Other Identifiers
ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs13142776
