以原型为导向的类条件集群运输,用于无监督域调整
Liangda Yan1, Jianwen Tao2, Tao He3
1School of Electronic Information, Zhejiang Business Technology Institute, Ningbo, 315012, Zhejiang, China.
Scientific reports
|October 29, 2025
概括
本研究介绍了类条件集群传输 (CLUST),一种新的无监督域适应方法. CLUST通过专注于域内结构来提高模型性能,以更好地聚合特征和域对齐.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 无监督域适应 (UDA) 对于面临不同数据分布的机器学习模型至关重要.
- 现有的UDA方法往往忽略了内部数据结构,限制了歧视权.
- 需要使用利用域内语义信息的UDA技术.
研究的目的:
- 引入一种新的UDA方法,即类条件集群运输 (CLUST),它解决了先前工作的局限性.
- 通过结合聚类目标和深度原型学习来提高UDA绩效.
- 提高UDA中概率输出的可靠性和多样性.
主要方法:
- CLUST采用了类条件特征集群和原型集群运输成本.
- 该方法最大限度地提高了各种输出的信息,并确保了语义一致性.
- 深度原型学习被用来促进域内特征聚合和调整域类结构.
主要成果:
- CLUST有效地降低了特征集群运输成本和原型集群运输成本.
- 该方法对同类样本保持一致的概率预测,保持语义一致性.
- 理论分析证实了CLUST架构在概括错误限制方面的稳定性和稳定性.
结论:
- 在多样化和具有挑战性的UDA场景中,CLUST展示了最先进的或可比的性能.
- 该方法在各种UDA应用中被证明是可靠和实用的.
- 在利用域内语义结构来改进UDA方面,CLUST提供了显著的进步.
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