Cross-Domain Attribute Representation Based on Convolutional Neural Network

  • Guohui Zhang
  • Gaoyuan LiangEmail author
  • Fang Su
  • Fanxin Qu
  • Jing-Yan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


In the problem of domain transfer learning, we learn a model for the prediction in a target domain from the data of both some source domains and the target domain, where the target domain is in lack of labels while the source domain has sufficient labels. Besides the instances of the data, recently the attributes of data shared across domains are also explored and proven to be very helpful to leverage the information of different domains. In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and attributes. We proposed to embed the attributes of different domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by different domains by matching across domains, and a domain-specific CNN model to represent the information of each domain. The concatenation of the three CNN model outputs is used to predict the class label. An iterative algorithm based on gradient descent method is developed to learn the parameters of the model. The experiments over benchmark datasets show the advantage of the proposed model.


Convolutional neural network Domain-Transfer learning Attribute embedding 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guohui Zhang
    • 1
  • Gaoyuan Liang
    • 2
    Email author
  • Fang Su
    • 3
  • Fanxin Qu
    • 4
  • Jing-Yan Wang
    • 5
  1. 1.Huawei Technologies Co., LtdShanghaiChina
  2. 2.Jiangsu University of TechnologyChangzhouChina
  3. 3.Shaanxi University of Science and TechnologyXi’anChina
  4. 4.Northwestern Polytechnical UniversityXi’anChina
  5. 5.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina

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