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Pairwise Generalization Network for Cross-Domain Image Recognition

  • Y. B. Liu
  • T. T. Han
  • Z. GaoEmail author
Article
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Abstract

In recent years, convolutional neural networks have received increasing attention from the computer vision and machine learning communities. Due to the differences in the distribution, tone and brightness of the training domain and test domain, researchers begin to focus on cross-domain image recognition. In this paper, we propose a Pairwise Generalization Network (PGN) for addressing the problem of cross-domain image recognition where Instance Normalization and Batch Normalization are added to enhance their abilities in the original domain and to expand to the new domain. Meanwhile, the Siamese architecture is utilized in the PGN to learn an embedding subspace that is discriminative, and map positive sample pairs aligned and negative sample pairs separated, which can work well even with only few labeled target data samples. We also add residual architecture and MMD loss for the PGN model to further improve its performance. Extensive experiments on two different public benchmarks show that our PGN solution significantly outperforms the state-of-the-art methods.

Keywords

Cross-domain Image recognition Pairwise 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology and Key Laboratory of Computer Vision and System, Ministry of EducationTianjin University of TechnologyTianjinChina
  2. 2.Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan)Shandong Artifical Intelligence InstituteJinanPeople’s Republic of China

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