基于自监督任务最优选择的无监督域自适应方法

doi:10.11896/jsjkx.201000030

摘要/Abstract

摘要： 无监督域自适应方法通过源域标签数据学习到的知识对目标域无标签数据进行分类,成为目前迁移学习中解决两个域特征对齐的主流方法。针对现实中存在已标签数据量少且质量不高造成提取的特征不完备的情况,文中提出了基于自监督任务最优选择的无监督域自适应方法。为使特征具有更强的语义信息,在两个域未标记数据上使用了多个自监督任务;此外,针对进行自监督任务时的易混淆特征,提出了一种新的智能组合优化策略自适应地选择有效特征;最后通过两个域沿着任务相关方向靠近使得源域标记数据训练的分类器能够更好地推广到目标域。仿真实验在公开的6个基准数据集上分别从分类精度、训练集数据使用量、自监督任务使用效果3个方面进行了对比分析。实验结果表明,所提方法在3个方面上的表现都优于现有的先进方法,使用相同数据集时分类精度提高8%;在相同的分类精度要求下,所用数据量减少12%;与单个自监督任务对比时精度提高了11%。

关键词: 迁移学习, 无监督域自适应, 语义信息, 智能组合优化策略, 自监督任务

Abstract: The unsupervised domain adaptation method uses the knowledge learned from the source domain label data to classify the target domain unlabeled data,which has become the mainstream method to solve the feature alignment of the two domains in transfer learning.In view of the fact that the amount of labeled data is small and the quality is not high,the extracted features are incomplete,this paper proposes an unsupervised domain adaptation method based on the optimal selection of self-supervised tasks.In order to make the features have stronger semantic information,multiple self-supervised tasks are used on the unlabeled data in the two domains.In addition,a new intelligent combination optimization strategy is proposed to adaptively select effective features for self-supervised tasks.Finally,the two domains are approached along the task-related direction so that the classifier trained on the source domain label data can be better promoted to the target domain.The simulation experiment conducts a comparative analysis on the six public benchmark datasets from three aspects:classification accuracy,training data volume,and self-supervised task use effect.Experimental results show that the proposed method outperforms the existing advanced methods in three aspects.The classification accuracy is improved by 8% when using the same datasets,and the amount of data used is reduced by 12% under the same classification accuracy requirements.Compared with a single self-supervised task,the accuracy is improved by 11%.

Key words: Intelligent combination optimization strategy, Self-supervised task, Semantic information, Transfer learning, Unsupervised domain adaptation

中图分类号:

TP391.41

吴兰, 王涵, 李斌全. 基于自监督任务最优选择的无监督域自适应方法[J]. 计算机科学, 2021, 48(6A): 357-363. https://doi.org/10.11896/jsjkx.201000030

WU Lan, WANG Han, LI Bin-quan. Unsupervised Domain Adaptive Method Based on Optimal Selection of Self-supervised Tasks[J]. Computer Science, 2021, 48(6A): 357-363. https://doi.org/10.11896/jsjkx.201000030

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