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FDC:非监督域的特征丢失一致性适应语义细分的语义细分.

Chaoyu Rao1, Wanshu Fan1, Cong Wang2

  • 1National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, China.

Neural networks : the official journal of the International Neural Network Society
|May 17, 2025
PubMed
概括

特征丢失一致性 (FDC) 通过扰乱特征来防止在不受监督的域适应语义细分中过度拟合. 这种新的方法增强了自我培训方法,在领域适应任务中设定了新的基准.

关键词:
一致性技术是一致性技术.扰动 扰动 扰动进行自我训练.语义细分 语义细分是指语义细分.无监督的域名适应

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Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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