模型参数自适应迁移的多源域适应

余欢欢 魏文戈

摘   要：在新领域中，常常存在样本不充分或标记不足的問题。针对此问题，人们提出了域适应，该方法利用相关领域（源域）的知识来提高当前领域（目标域）学习性能。单个源域的知识往往不充分且类别完全相同的多个源域难以满足，同时域之间存在漂移问题。而现有的多源域适应模型难以解决类别不完全一致的问题，因此给多源域适应带来了较大的挑战。为此提出了一种基于模型参数自适应迁移的方法（Adaptive Transfer for ModelParameter，ATMP），通过对每个源域的模型参数进行私有和公有模型参数字典学习，同时将多个源域中所学的模型参数字典作为目标域的模型参数字典，然后通过对字典系数的行稀疏约束实现源域和目标域模型参数的自适应选择。除此之外，该方法迁移的是模型参数而不是数据本身，因此有效实现了对源域数据的隐私保护。经过一系列实验表明，在相关数据集上的实验显示了本文所提方法在聚类性能上的显著有效性。

关键词：多源域适应;模型参数自适应迁移;隐私保护;聚类

中图分类号：TP391                               文献标识码：A

Model Parameter Transfer Adaptively for Multi-source Domain Adaptation

YU Huan-huan WEI Wen-ge

（College of Computer Science and Technology，Nanjing University of Aeronautics and Astronautics，Nanjing，Jiangsu 211106，China）

Abstract：In new fields，there is often the problem of insufficient samples or labels. For this problem，domain adaptation（DA） has been proposed，which uses the knowledge of the related domain （source domain） to improve the learning performance of the current domain （target domain）. The knowledge of a single source domain is often insufficient and multiple source domains with identical categories are difficult to satisfy，and there is a shift problem between domains. However，the existing multi-source TL model is hard to solve the problem of sharing inconsistent categories，which brings a great challenge for multi-source TL. Aiming at this problem，an adaptive transfer for model parameter method has been proposed，which can learn the private and public model parameter dictionary in each source domain. The model parameter dictionary learned in the source domains are used as the target ones，and then the model parameter of source domains and target domain are selected adaptively by the row sparse constraint of the dictionary coefficients. In addition，the method can also be directly used for TL of privacy protection due to the fact that the knowledge is transferred just via the model parameters rather than data itself. After a series of experiments，the experiments on the relevant datasets show the significant effectiveness of the proposed method in clustering performance.

Key words：multi-source domain adaptation;model parameter transfer adaptively;privacy protection;clustering

在传统的机器学习中，通常假设训练数据（称为源域）和测试数据（称为目标域）独立同分布，但在现实场景中，由于不同的因素，比如视角，展现模式的区别等，这些因素都可能导致该假设难以满足。因此，学者们提出了域适应，通过迁移源域的知识到目标域来避免源域和目标域数据分布不一致的问题。目前域适应主要包括单源域到单目标域知识的迁移，多源域到单目标域知识的迁移。……