Deep Contrastive Representation Learning With Self-Distillation
Article Properties
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DOI (url)
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Publication Date2024/02/01
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Indian UGC (journal)
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Refrences76
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Citations2
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Zhiwen Xiao School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Huanlai Xing School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Bowen Zhao School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Rong Qu School of Computer Science, University of Nottingham, Nottingham, U.K. ORCID (unauthenticated)
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Shouxi Luo School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Penglin Dai School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Ke Li School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
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Zonghai Zhu School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China ORCID (unauthenticated)
Cite
Xiao, Zhiwen, et al. “Deep Contrastive Representation Learning With Self-Distillation”. IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, 2024, pp. 3-15, https://doi.org/10.1109/tetci.2023.3304948.
Xiao, Z., Xing, H., Zhao, B., Qu, R., Luo, S., Dai, P., Li, K., & Zhu, Z. (2024). Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 3-15. https://doi.org/10.1109/tetci.2023.3304948
Xiao, Zhiwen, Huanlai Xing, Bowen Zhao, Rong Qu, Shouxi Luo, Penglin Dai, Ke Li, and Zonghai Zhu. “Deep Contrastive Representation Learning With Self-Distillation”. IEEE Transactions on Emerging Topics in Computational Intelligence 8, no. 1 (2024): 3-15. https://doi.org/10.1109/tetci.2023.3304948.
Xiao Z, Xing H, Zhao B, Qu R, Luo S, Dai P, et al. Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence. 2024;8(1):3-15.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Backpropagation | 2011 | |||
| Contrastive representation learning for electroencephalogram classification | 2020 | |||
| A simple framework for contrastive learning of visual representations | 2020 | |||
| Unsupervised scalable representation learning for multivariate time series | 2019 | |||
| Learning representations for time series clustering | 2019 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Load balancing of multi-AGV road network based on improved Q-learning algorithm and macroscopic fundamental diagram | Complex & Intelligent Systems |
| 2024 | |
| Densely Knowledge-Aware Network for Multivariate Time Series Classification | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| 2024 |
Citations Analysis
The category
Technology: Mechanical engineering and machinery 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Load balancing of multi-AGV road network based on improved Q-learning algorithm and macroscopic fundamental diagram and was published in 2024. The most recent citation comes from a 2024 study titled Load balancing of multi-AGV road network based on improved Q-learning algorithm and macroscopic fundamental diagram. This article reached its peak citation in 2024, with 2 citations. It has been cited in 2 different journals, 50% of which are open access. Among related journals, the Complex & Intelligent Systems cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.