储备适应:开放式域名适应采矿类间关系的开放式域名适应
概括
这项研究引入了开放式域调整 (OSDA) 的新方法,通过为未知类保留嵌入空间,提高模型调整的准确性. 该方法利用源域的类间关系来增强目标域的类分离.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 开放式域调整 (OSDA) 解决了目标域中未知类的模型调整挑战.
- 现有的OSDA方法经常与未知的类的负转移相斗争.
- 一个关键的挑战是有效地处理未知的类,而不会影响已知的类性能.
研究的目的:
- 提出一种新的OSDA方法,为未知类保留嵌入空间.
- 利用源域类间关系来改善目标域的适应性.
- 为了减轻OSDA中未知的类所造成的负转移.
主要方法:
- 为未知类保留嵌入间的空间,而不是将它们视为单个实体.
- 收紧已知的类表示,并在源域中扩大类间边缘.
- 在源域中学习软标签原型,以区分目标域中的已知和未知样本.
- 代地应用这些步骤,以相互增强空间预订和未知样本歧视.
主要成果:
- 在标准的OSDA数据集上取得了最先进的结果:Office-31,Office-Home,VisDA和ImageCLEF.
- 证明了为未知类保留嵌入空间的有效性.
- 提供分析以了解拟议方法的行为和好处.
结论:
- 拟议的方法通过利用源域知识,有效地为未知类保留空间.
- 这种方法通过避免负转移来改善开放场景中的模型适应性.
- 这些发现为处理域调整中未知的类提供了新的视角.
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