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通过规范化流程逐步调整域名
Shogo Sagawa1,2, Hideitsu Hino3,4
1Department of Statistical Science, Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan sagawa@ism.ac.jp.
Neural computation
|January 9, 2025
概括
本研究引入了规范化流程,以改善当存在大域差距时的逐渐域适应. 该方法通过转换目标域分布来提高分类性能,克服传统自我训练方法的局限性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 标准域调整方法在源数据和目标数据域之间存在显著差异.
- 逐步域名适应使用中间域名,但在有限的中间步骤和大域名距离的情况下经常失败.
研究的目的:
- 为了解决在具有挑战性的领域差距下逐步域名适应的失败.
- 提出一种新的方法,利用无监督领域适应中的规范化流.
主要方法:
- 拟议的方法使用规范化流来学习从目标域分布到高斯混合分布的转换,利用源域.
- 这种方法保持了无监督域调整框架.
主要成果:
- 在真实世界数据集上的实验证明了该方法在缓解由大域差距引起的问题方面的有效性.
- 与现有方法相比,拟议的技术显著提高了分类性能.
结论:
- 规范化流提供了一个可行的解决方案,以逐渐的域适应问题与大域差距.
- 该方法成功地提高了对挑战无监督域调整场景的分类准确性.
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