Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230100068-8.doi: 10.11896/jsjkx.230100068

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Image Classification for Unsupervised Domain Adaptation Based on Task Relevant FeatureSeparation Network

TANG Junkun1, ZHANG Hui2, ZHANG Zhouquan1and WU Tianyue1   

  1. 1 School of Electrical & Information Engineering,Changsha University of Science & Technology,Changsha 410000,China
    2 School of Robotics,Hunan University,Changsha 410000,China
  • Published:2023-11-09
  • About author:TANG Junkun,born in 1997,postgra-ducate.His main research interests include image processing,deep learning,transfer learning,and domain adaptation.
    ZHANG Hui,born in 1983,Ph.D,professor.His main research interests include machine vision,image processing,and robot vision.
  • Supported by:
    Major Research Plan of the National Natural Science Foundation of China(92148204),National Key R&D Program of China(2021ZD0114503),National Natural Science Foundation of China(61971071,62027810,62133005),Hunan Science Fund for Distinguished Young Scholars(2021JJ10025),Hunan Key Research and Development Program(2021GK4011,2022GK2011),Changsha Science and Technology Major Project(kh2003026),Joint Open Foundation of State Key Laboratory of Robotics(2021-KF-22-17),China University Industry-University-research Innovation Fund(2020HYA06006) and Hunan Leading Talent of Technological Innovation(2022RC3063).

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Abstract: Unsupervised domain adaptation(UDA) aims to assist the model in transferring learned information from a labeled source domain to an unlabeled target domain,given cross-domain distribution discrepancies.Current advanced domain adaptation techniques rely mostly on aligning the distributions of the source and target domains.Among them,the features are frequently utilized as a global object to perform inter-domain adaptation tasks,disregarding the coupling of task-related information(inter-domain invariant,intra-domain specific information) and task-irrelevant information(color contrast,image style) in the features.The situation makes it difficult for the model to comprehend the important information of features,resulting in sub-optimization.In consideration of the aforementioned issues,we propose an unsupervised domain adaptive classification method based on a task relevant feature separation network(TRFS),which helps the network extract the downstream task-related feature weight by learning the attention consistency between the features with inter-domain style mixed interference and the original features.Further,weight subtraction is used to obtain the task-irrelevant feature weight,then the task-related and irrelevant features are further pushed away by orthogonal function constraints to achieve feature separation.The task feature refinement separation layer is designed to reduce the confusion situation of alignment features and domain-specific features,as well as optimize the model’s classification and discrimination accuracy.Comprehensive experiment results demonstrate that the designed separation module has good plug-and-play performance,which can enhance the performance of other UDA methods.And the TRFS has obvious advantages over other advanced UDA methods,which achieves a classification accuracy of 73.6% in the Office-Home benchmark.

Key words: Feature decoupling, Task-relevant, Attention mechanism, Unsupervised domain adaptation, Image classification

CLC Number: 

  • TP391
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