给分类带来公平:对域链接类的域泛化
IEEE transactions on pattern analysis and machine intelligence
|August 11, 2025
概括
域名通用化与域名链接类扎. 我们介绍了FOND,一种使用对比学习的新方法,以改善这些具有挑战性的课堂的知识传递,实现最先进的结果.
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:
- 域泛化 (DG) 旨在将知识从源域转移到未见的目标域.
- 标准 DG 假设类在多个域中存在,以避免虚假的相关性.
- 数据稀缺性往往导致域链接类,阻碍了概括.
研究的目的:
- 介绍一个具有挑战性的领域相关的总局任务.
- 开发一种方法来学习对域链接类的域不变表示.
- 在面对特定领域的类别时,解决现有总局方法的局限性.
主要方法:
- 提议FOND (为Domain-linked DG提供以公平为灵感和对比的学习目标).
- 使用对比式学习来弥合域共享和域链接类.
- 制定一个以公平为灵感的目标,以减轻负面转移.
主要成果:
- 基金在与领域相关的总局任务上取得了最先进的表现.
- 显示了对域链接类的显著改进.
- 显示域共享类的最小性能权衡.
结论:
- 拟议的FOND方法有效地解决了与领域相关的总局挑战.
- 强调处理域特定类对于强大的概括的重要性.
- 提供了理论见解和实践指南,用于域链接类的泛化.
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