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Mini-batch Dynamic Geometric Embedding for Unsupervised Domain Adaptation

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Abstract

Unsupervised domain adaptation has gotten a lot of attention due to its ability to improve learning performance in a target domain based on the knowledge extracted from a source domain. Recent studies show that graph-based models can accomplish good results for domain adaptation problems. However, most of these graph-based domain adaptation approaches cannot work in an end-to-end manner, leading to the limited scalable. To address this issue, we propose a learning method named Mini-batch Dynamic Geometric Embedding (MDGE), which seeks to find the relationship between batches source and target samples to learn discriminative representations. Specifically, to build a better graph representing sample relationship, we propose a class-specific sampling strategy to pick up samples which are then used as input of MDGE. Since the samples are effectively selected, we develop a method to dynamically build a subgraph that in turn supports the relationship update and helps the network backbone to extract more discriminative features. Comprehensive experiments on real-world visual datasets demonstrate the effectiveness of the proposed MDGE algorithm.

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Notes

  1. https://www.imageclef.org/2014/adaptation.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) 62272172, the Major Scientific and Technological Innovation Project of Shandong Province of China (2021ZLGX05, 2020CXGC010705), Guangdong Basic and Applied Basic Research Foundation 2023A1515012920, Tiptop Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program 2019TQ05X200 and 2022 Tencent Wechat Rhino-Bird Focused Research Program (Tencent WeChat RBFR2022008), and the Major Key Project of PCL under Grant PCL2021A09.

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Siraj Khan & Qingyao Wu

  2. Pazhou Lab, Guangzhou, China

    Siraj Khan

  3. Peng Cheng Laboratory, Shenzhen, China

    Qingyao Wu

  4. Guangzhou Institute of Science and Technology, Guangzhou, China

    Yuxin Guo

  5. City University of Hong Kong, Hong Kong, China

    Yuzhong Ye

  6. School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China

    Chunshan Li

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  1. Siraj Khan
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  4. Chunshan Li
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  5. Qingyao Wu
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Correspondence to Chunshan Li or Qingyao Wu.

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Siraj Khan and Yuxin Guo contributed equally to this work.

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Khan, S., Guo, Y., Ye, Y. et al. Mini-batch Dynamic Geometric Embedding for Unsupervised Domain Adaptation. Neural Process Lett 55, 2063–2080 (2023). https://doi.org/10.1007/s11063-023-11167-7

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