Works (3)
Updated: June 7th, 2023 12:24
2021 article
Local Clustering with Mean Teacher for Semi-supervised learning
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp. 6243–6250.
TL;DR:
This work proposes a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias in the Mean Teacher model and demonstrates on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding the LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi- supervised learning.
(via Semantic Scholar)
arxiv.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
4. Quality Education
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 30, 2021
2017 conference paper
Hierarchical change detection framework for biomass monitoring
2017 ieee international geoscience and remote sensing symposium (igarss), 620–623.
TL;DR:
A nearest neighbor based hierarchical change detection methodology for analyzing multi-temporal remote sensing imagery and shows that K-Means over-detects changes in comparison to the proposed method.
(via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
2016 conference paper
Scalable nearest neighbor based hierarchical change detection framework for crop monitoring
2016 IEEE International Conference on Big Data (Big Data), 1309–1314.
UN Sustainable Development Goal Categories
13. Climate Action
(OpenAlex)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.