通过内核统计距离估计进行部分域调整的联合重量优化
1Department of Computer Science, Shantou University, China.
概括
本研究介绍了部分域适应 (PDA) 的联合重量优化 (JWO),通过匹配联合分布而不是边际分布来改善神经网络传输. 该方法可以在具有有限标记数据的目标域上提高模型性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 部分域调整 (PDA) 旨在将神经网络从源到目标域,其中源标签空间包括目标标签空间.
- 现有的方法侧重于匹配边际分布,这是次优的,因为目标性能取决于联合分布对齐.
研究的目的:
- 为部分域调整提出一个联合重量优化 (JWO) 方法.
- 通过关注特征空间中的联合分布对齐来解决边际分布匹配的局限性.
主要方法:
- 开发了一种联合重量优化 (JWO) 方法,以调整加权的联合源和目标分布.
- 利用L2-距离和 χ2-分歧来测量联合分布差异.
- 拟议的内核统计距离估计 (KSDE) 来估计这些距离的数据和优化联合权重.
主要成果:
- 联合分销组织 (JWO) 的方法有效地减少了联合分销的差异.
- 使用优化权重对加权源数据进行神经网络训练,提高了目标域的性能.
- 在受欢迎的数据集上的实验证明了拟议方法的有效性.
结论:
- 联合分布对齐对于有效的部分域调整至关重要.
- 拟议的JWO和KSDE方法为PDA提供了一种新且有效的解决方案.
- 这种方法有助于在领域适应情景中更好地转移知识.
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