CAT:针对类的自适应值,以实现强大的半监督域泛化
Sumaiya Zoha1, Jeong-Gun Lee2, Young-Woong Ko2
1Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
PloS one
|September 4, 2025
概括
这项研究介绍了CAT,一种使用自适应值和伪标签改进的新型半监督域泛化方法. 它通过有限的标记数据实现了强大的通用化性能,克服了领域转移的挑战.
科学领域:
- 计算机视觉
- 机器学习
- 人工智能
背景情况:
- 域泛化 (DG) 旨在跨域转移知识,但需要广泛的标记数据.
- 高质量的标记数据是昂贵和劳动密集的,限制了实际的GD应用.
- 半监督域名通用化 (SSDG) 提供了一个标签效率高的替代方案.
研究的目的:
- 在一个标签效率的范式下研究一个实际的SSDG问题.
- 提出一种新的方法,CAT,用于具有有限标记数据的竞争性概括性能.
- 解决以前方法的局限性,包括固定的门和噪音伪标签.
主要方法:
- 使用有限的标记数据进行半监督学习.
- 采用适应性值来产生高质量的伪标签,并具有类别多样性.
- 使用噪音标签精细化技术来提高伪标签的可靠性.
主要成果:
- 在域名转移的情况下,CAT实现了竞争性通用化性能.
- 在基准数据集上表现优异:PACS (+3.45%),OfficeHome (+9.47%) 和miniDomainNet (+10.90%).
- 突出了尽管领域的转变,但在实现强大的概括方面的有效性.
结论:
- 对于SSDG任务,CAT提供了一个简单而高效的解决方案.
- 这种方法成功地克服了对固定门的依赖和对杂伪标签的敏感性.
- 在标签效率高的环境中实现了强大的通用化,提高了GD的实际适用性.
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