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| Heterogeneous domain adaptation based on pseudo label refinement and semantic alignment |
Lan WU( ),Quan-long CUI |
| College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China |
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Abstract A new heterogeneous domain adaptive method was proposed for the existing methods ignoring the importance of semantic attributes in pseudo-labeling and category prediction, resulting in poor classification accuracy. The conditional and the marginal distributions of the source and the target domains were matched, and the similarity of the source and the target data in the common feature subspace was considered. The classification accuracy of a model was increased, enhancing the confidence of the pseudo-labels of the target domain pseudo-labels by refining the pseudo-labels of the spatial similarity. The domain discriminator in the semantic prediction space was constructed by the similar predictive distributions of the similar samples after the classifier output, and the generalization of the model was improved. Experimental results of the classification task for text and images with different feature representations successfully validate the superiority of the proposed method.
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Received: 24 November 2022
Published: 16 October 2023
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| Fund: 国家自然科学基金资助项目（61973103）；河南省优秀青年基金资助项目（222300420039）；郑州市科技创新协同专项重点项目（21ZZXTCX01） |
基于伪标签细化和语义对齐的异构域自适应
在进行跨域知识迁移时，现有异构域自适应方法忽略了伪标签和类别预测中语义属性的重要性，导致分类精度不高，为此提出新的异构域自适应方法. 该方法匹配源域和目标域的条件分布和边缘分布，考虑源数据和目标数据在公共特征子空间的相似性，通过细化空间相似性的伪标签来增强目标域伪标签的置信度，使模型的分类精度提高. 考虑同类的样本经过分类器输出后有相似的预测分布，构造语义预测空间中的域鉴别器，使模型的泛化性提升. 不同特征表示的文本和图像的分类任务实验结果成功地验证了所提方法的优越性.
关键词：
异构域自适应,
伪标签细化,
语义预测空间,
域鉴别器,
语义对齐
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