通过代对比式学习和集群,实现向开放世界领域的适应
概括
本研究介绍了开放世界的域调整 (DA),以识别目标数据中的已知类和发现新类. 拟议的对比学习框架有效地集群数据,减少域差异,提高开放世界的DA性能.
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:
- 开放式域调整 (DA) 解决了源域和目标域之间的共变量和类别转移.
- 现有的方法往往无法在目标域中发现新型类,将它们标记为"未知".
研究的目的:
- 介绍一个更具挑战性的开放世界 DA 问题:识别已见类,同时发现新类.
- 为开放世界DA提出一个新的框架,利用聚类和对比学习.
主要方法:
- 该框架将问题转换为一个集群任务,使用对比学习来建模实例关系.
- 它采用了一种代过程,涉及半监督的集群和对比的学习步骤.
- 该方法可以通过预期最大化 (EM) 算法进行优化.
主要成果:
- 拟议的方法在五个公共数据集中实现了卓越的性能.
- 它有效地将未标记的目标数据分为已见类和新类.
- 对比损失减少域差异,并促进新类的发现.
结论:
- 开发的框架成功地解决了开放世界的DA问题.
- 这项工作为未来对开放世界领域适应的研究建立了新的基准.
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