有证据的图形对比对准源无混合-目标域调整的对比对齐
IEEE transactions on neural networks and learning systems
|September 8, 2025
概括
本研究介绍了无源混合-目标域适应 (SF-BTDA) 的证据图对比对齐 (EGCA). EGCA有效地处理无标签的多个目标域,改善伪标签质量并减少标签转移问题.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 域调整 (DA) 面临着现实世界数据异质性的挑战.
- 现有的DA方法在源码自由设置和多个未标记的目标域中扎.
- 无源混合目标域调整 (SF-BTDA) 呈现了一个现实的,但复杂的场景,带有混合的标签转移和杂的伪标签.
研究的目的:
- 为SF-BTDA设置提出一种新的方法,即证据图对比对齐 (EGCA).
- 为了应对不可访问的源数据,未标记的多个目标域和杂的伪标签的挑战.
- 提高伪目标标签的准确性和确定性,并尽量减少混合目标域内的分布差距.
主要方法:
- 校准证据学习 (CEL) 模块用于代改进模型准确性和确定性,生成高质量的伪目标标签.
- 图形对比学习包含域距离矩阵和信任-不确定性标准,以对准混合目标中的样本分布.
- 开发一个新的基准,使用三个标准的DA数据集来评估拟议的方法.
主要成果:
- 在SF-BTDA设置中,EGCA的表现明显优于现有方法.
- 提出的方法实现了相当大的性能增长.
- EGCA的结果与访问源数据或域名标签的方法相似.
结论:
- 对于具有挑战性的SF-BTDA问题,EGCA提供了一个有效的解决方案.
- 该方法成功地分离了混合目标域,并减轻了杂的伪标签的影响.
- 埃格卡在复杂的现实场景的领域适应方面推进了最先进的技术.
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