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Updated: Jun 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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BPT-PLR:一个平衡的分区和培训框架与伪标签放松的对比损失噪音标签学习

Qian Zhang1, Ge Jin1,2, Yi Zhu1

  • 1School of Information Technology, Jiangsu Open University, Nanjing 210036, China.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
概括

这项研究引入了一个新的框架,BPT-PLR,以改善噪音标签的数据集上的深度神经网络性能. 它解决了阶级不平衡和优化冲突,在杂的标签学习中取得最佳结果.

关键词:
高斯混合物模型模型的高斯混合物模型.深度神经网络是一个神经网络.有噪音的标签伪标签放松了对比的损失.

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