域名通用化多元化目标和贡献安排
概括
本研究引入了多元目标和贡献调度 (DTCS),通过使用软标签和平衡源域贡献来改进域泛化 (DG),克服计算机视觉中标准一热标签的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 在分布转移下进行概括是计算机视觉中的一个主要挑战.
- 目前使用一热标签的域泛化 (DG) 方法可能会导致梯度冲突,并且无法捕捉内在类特征.
- 现有总局的方法往往忽视了源域的独特贡献,导致学习不平衡.
研究的目的:
- 解决 DG.的梯度冲突和不均的源域学习问题.
- 为总局提出一个名为多元目标和贡献安排 (DTCS) 的新模式.
- 分析分布变化和 DG. 的梯度冲突之间的关系.
主要方法:
- 拟议的多元化目标和贡献调度 (DTCS) 范式用于总局.
- 引入多元目标监管 (DTS),使用不同的软标签来缓解梯度冲突.
- 实现多元贡献平衡 (DCB) 以动态平衡源域贡献.
主要成果:
- DTCS有效地解决了 DG.中的一热标签和相同源域贡献的局限性.
- 在四个基准数据集上的实验证明了与最先进的方法相比具有竞争力的性能.
- 拟议的方法在域泛化任务中显示出有效性和优势.
结论:
- 通过考虑经验源域风险,DTCS为GD提供了一个新的视角.
- 该方法成功地减轻了梯度冲突,并改善了类内变化.
- DTCS代表了域泛化研究的重大进展.
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