Optimization Strategies for Self-Supervised Learning in the Use of Unlabeled Data Article Swipe

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Haopeng Zhao , Yan Lou , Qiming Xu , Zheng Feng , Ying Wu , Tao Huang , LiangHao Tan , Zichao Li ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.53469/jtpes.2024.04(05).05

This study explores optimization strategies for self-supervised learning in the use of unlabeled data. By deeply analyzing existing research, we propose a novel method that significantly enhances the performance of algorithms on unlabeled data, achieving improved accuracy and generalization capabilities. Our method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. We also discuss how to optimize self-supervised learning strategies in the use of unlabeled data. Through improvements and optimizations of self-supervised learning algorithms, we introduce a new method for effectively utilizing unlabeled data for model training. Experimental results show significant performance improvements across various datasets, highlighting the method's robust generalization ability. This research is significant for advancing self-supervised learning technologies, providing valuable insights for related fields.

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Computer Science

Machine Learning

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Artificial Intelligence

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Computer science Machine learning Generalization Artificial intelligence Semi-supervised learning Labeled data Supervised learning Artificial neural network Mathematics Mathematical analysis

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Type: article
Language: en
Landing Page: https://doi.org/10.53469/jtpes.2024.04(05).05
PDF: https://centuryscipub.com/index.php/jtpes/article/download/588/499
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Cited By: 18
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OpenAlex ID: https://openalex.org/W4399539839

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OpenAlex ID: https://openalex.org/W4399539839

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DOI: https://doi.org/10.53469/jtpes.2024.04(05).05

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Title: Optimization Strategies for Self-Supervised Learning in the Use of Unlabeled Data

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Type: article

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Language: en

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Publication year: 2024

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Publication date: 2024-05-23

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Authors: Haopeng Zhao, Yan Lou, Qiming Xu, Zheng Feng, Ying Wu, Tao Huang, LiangHao Tan, Zichao Li

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Landing page: https://doi.org/10.53469/jtpes.2024.04(05).05

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Concepts: Computer science, Machine learning, Generalization, Artificial intelligence, Semi-supervised learning, Labeled data, Supervised learning, Artificial neural network, Mathematics, Mathematical analysis

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Cited by: 18

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