Nothing Special   »   [go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Joint marginal and central sample learning for domain adaptation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Domain adaptation aims to alleviate the impact of distribution differences when migrating knowledge from the source domain to the target domain. However, two issues remain to be addressed. One is the difficulty of learning both marginal and specific knowledge at the same time. The other is the low quality of pseudo labels in target domain can constrain the performance improvement during model iteration. To solve the above problems, we propose a domain adaptation method called Joint Marginal and Central Sample Learning (JMCSL). This method consists of three parts which are marginal sample learning (MSL), central sample learning (CSL) and unified strategy for multi-classifier (USMC). MSL and CSL aim to better learning of common and specific knowledge. USMC improves the accuracy and stability of pseudo labels in the target domain. Specifically, MSL learns specific knowledge from a novel triple distance, which is defined by sample pair and their class center. CSL uses the closest class center and the second closest class center of samples to retain the common knowledge. USMC selects label consistent samples by applying K-Nearest Neighbors (KNN) and Structural Risk Minimization (SRM), while it utilizes the class centers of both two domains for classification. Finally, extensive experiments on four visual datasets demonstrate that JMCSL is superior to other competing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availibility

Data is provided within the manuscript or supplementary information files.

References

  1. Ren, Q., Mao, Q., Lu, S.: Prototypical bidirectional adaptation and learning for cross-domain semantic segmentation. IEEE Trans. Multimed. (2023)

  2. Wu, X., Wu, Z., Ju, L., Wang, S.: A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 58–72 (2021)

    Article  Google Scholar 

  3. Xu, G., Huang, C., Silva, D.S., Albuquerque, V.H.C.: A compressed unsupervised deep domain adaptation model for efficient cross-domain fault diagnosis. IEEE Trans. Ind. Inform. (2022)

  4. Yang, B., Lei, Y., Li, X., Roberts, C.: Deep targeted transfer learning along designable adaptation trajectory for fault diagnosis across different machines. IEEE Trans. Industr. Electron. 70(9), 9463–9473 (2022)

    Article  Google Scholar 

  5. Cao, Y., Long, M., Wang, J.: Unsupervised domain adaptation with distribution matching machines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

  6. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)

    Article  Google Scholar 

  7. Zhang, L., Gao, X.: Transfer adaptation learning: A decade survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)

  8. Patil, D.R., Pattewar, T.M.: Majority voting and feature selection based network intrusion detection system. EAI Endorsed Trans. Scalable Inf. Syst. 9(6), 6–6 (2022)

    Google Scholar 

  9. Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1210–1215 (2019). IEEE

  10. Xiao, T., Liu, P., Zhao, W., Liu, H., Tang, X.: Structure preservation and distribution alignment in discriminative transfer subspace learning. Neurocomputing 337, 218–234 (2019)

    Article  Google Scholar 

  11. Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., Hengel, A., Zhang, Y.: Unsupervised domain adaptation using robust class-wise matching. IEEE Trans. Circuits Syst. Video Technol. 29(5), 1339–1349 (2018)

    Article  Google Scholar 

  12. You, M., Yin, J., Wang, H., Cao, J., Wang, K., Miao, Y., Bertino, E.: A knowledge graph empowered online learning framework for access control decision-making. World Wide Web 26(2), 827–848 (2023)

    Article  Google Scholar 

  13. Vascon, S., Aslan, S., Torcinovich, A., Laarhoven, T., Marchiori, E., Pelillo, M.: Unsupervised domain adaptation using graph transduction games. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2019). IEEE

  14. Li, J., Jing, M., Lu, K., Zhu, L., Shen, H.T.: Locality preserving joint transfer for domain adaptation. IEEE Trans. Image Process. 28(12), 6103–6115 (2019)

    Article  MathSciNet  Google Scholar 

  15. Lu, H., Shen, C., Cao, Z., Xiao, Y., Hengel, A.: An embarrassingly simple approach to visual domain adaptation. IEEE Trans. Image Process. 27(7), 3403–3417 (2018)

    Article  MathSciNet  Google Scholar 

  16. Liang, J., He, R., Sun, Z., Tan, T.: Distant supervised centroid shift: A simple and efficient approach to visual domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2975–2984 (2019)

  17. Li, S., Song, S., Huang, G., Ding, Z., Wu, C.: Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Trans. Image Process. 27(9), 4260–4273 (2018)

    Article  MathSciNet  Google Scholar 

  18. Li, Y., Li, D., Lu, Y., Gao, C., Wang, W., Lu, J.: Progressive distribution alignment based on label correction for unsupervised domain adaptation. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2021). IEEE

  19. Hatefi, E., Karshenas, H., Adibi, P.: Distribution shift alignment in visual domain adaptation. Expert Syst. Appl. 235, 121210 (2024)

    Article  Google Scholar 

  20. Yang, L., Lu, B., Zhou, Q., Su, P.: Unsupervised domain adaptation via re-weighted transfer subspace learning with inter-class sparsity. Knowl.-Based Syst. 263, 110277 (2023)

    Article  Google Scholar 

  21. Wang, W., Li, Z., Li, W.: Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving. Neural Netw. 170, 427–440 (2024)

    Article  Google Scholar 

  22. Yao, S., Kang, Q., Zhou, M., Rawa, M.J., Albeshri, A.: Discriminative manifold distribution alignment for domain adaptation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 53(2), 1183–1197 (2022)

    Article  Google Scholar 

  23. Chang, H., Zhang, F., Ma, S., Gao, G., Zheng, H., Chen, Y.: Unsupervised domain adaptation based on cluster matching and fisher criterion for image classification. Computers & Electrical Engineering 91, 107041 (2021)

    Article  Google Scholar 

  24. Du, Y., Zhou, Y., Xie, Y., Zhou, D., Shi, J., Lei, Y.: Unsupervised domain adaptation via progressive positioning of target-class prototypes. Knowl.-Based Syst. 273, 110586 (2023)

    Article  Google Scholar 

  25. Meng, M., Lan, M., Yu, J., Wu, J., Liu, L.: Dual-level adaptive and discriminative knowledge transfer for cross-domain recognition. IEEE Trans. Multimed. (2022)

  26. Du, Y., Zhou, D., Shi, J., Lei, Y., Gong, M.: Dynamic-graph-based unsupervised domain adaptation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2021). IEEE

  27. Wang, W., Shen, Z., Li, D., Zhong, P., Chen, Y.: Probability-based graph embedding cross-domain and class discriminative feature learning for domain adaptation. IEEE Trans. Image Process. 32, 72–87 (2022)

    Article  Google Scholar 

  28. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV 11, pp. 213–226 (2010). Springer

  29. Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018–5027 (2017)

  30. Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066–2073 (2012). IEEE

  31. Wang, Y., Wang, H.: Distributionally robust unsupervised domain adaptation. J. Comput. Appl. Math. 436, 115369 (2024)

    Article  MathSciNet  Google Scholar 

  32. Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1129–1134 (2017). IEEE

  33. Ji, K., Zhang, Q., Zhu, S.: Subdomain alignment based open-set domain adaptation image classification. J. Vis. Commun. Image Represent. 98, 104047 (2024)

    Article  Google Scholar 

  34. Wang, H., Yang, Y., Liu, B.: Gmc: Graph-based multi-view clustering. IEEE Trans. Knowl. Data Eng. 32(6), 1116–1129 (2019)

    Article  Google Scholar 

  35. Zheng, Z., Teng, L., Zhang, W., Wu, N., Teng, S.: Knowledge transfer learning via dual density sampling for resource-limited domain adaptation. IEEE/CAA Journal of Automatica Sinica 10(12), 2269–2291 (2023)

    Article  Google Scholar 

  36. Zhang, W., Yang, X., Teng, S., Wu, N.: Semantic-guided hashing learning for domain adaptive retrieval. World Wide Web 26(3), 1093–1112 (2023)

    Article  Google Scholar 

  37. Teng, S., Zheng, Z., Wu, N., Teng, L., Zhang, W.: Adaptive graph embedding with consistency and specificity for domain adaptation. IEEE/CAA Journal of Automatica Sinica 10(11), 2094–2107 (2023)

    Article  Google Scholar 

  38. Zhou, L., Li, N., Ye, M., Zhu, X., Tang, S.: Source-free domain adaptation with class prototype discovery. Pattern Recogn. 145, 109974 (2024)

    Article  Google Scholar 

  39. Lü, S., Li, Z., Zhang, X., Li, J.: Consistency regularization-based mutual alignment for source-free domain adaptation. Expert Syst. Appl. 241, 122577 (2024)

    Article  Google Scholar 

  40. Zhang, W., Wu, D.: Discriminative joint probability maximum mean discrepancy (djp-mmd) for domain adaptation. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020). IEEE

  41. Sun, J., Wang, Z., Wang, W., Li, H., Sun, F.: Domain adaptation with geometrical preservation and distribution alignment. Neurocomputing 454, 152–167 (2021)

    Article  Google Scholar 

  42. Teng, S., Zheng, Z., Wu, N., Fei, L., Zhang, W.: Domain adaptation via incremental confidence samples into classification. Int. J. Intell. Syst. 37(1), 365–385 (2022)

    Article  Google Scholar 

  43. Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: International Conference on Machine Learning, pp. 7404–7413 (2019). PMLR

  44. Yang, G., Xia, H., Ding, M., Ding, Z.: Bi-directional generation for unsupervised domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6615–6622 (2020)

  45. Li, Y., Liu, Y., Zheng, D., Huang, Y., Tang, Y.: Discriminable feature enhancement for unsupervised domain adaptation. Image and Vision Computing, 104755 (2023)

  46. Teng, S., Liu, W., Guo, R., Zhang, W., Zheng, Z., Teng, L., Chen, T.: Domain adaptation with sample relation reinforcement. In: International Conference on Web Information Systems Engineering. pp. 937–952. Springer (2023)

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61972102.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, GuangZhou, 510006, GuangDong, China

    Shaohua Teng, Wenjie Liu, Zefeng Zheng & Wei Zhang

  2. Guangdong Provincial Laboratory of Chemistry and Fine Chemical Engineering Jieyang Center, 515200, Jieyang, China

    Shaohua Teng

  3. School of Information Engineering, Guangzhou Panyu Polytechnic, GuangZhou, 511483, GuangDong, China

    Luyao Teng

Authors
  1. Shaohua Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luyao Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zefeng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shaohua Teng and Wenjie Liu wrote the main manuscript text, Luyaoteng Teng and Zefeng Zheng were mainly responsible for data analysis, and Wei Zhang prepared the images.

Corresponding author

Correspondence to Luyao Teng.

Ethics declarations

Ethics approval

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Teng, S., Liu, W., Teng, L. et al. Joint marginal and central sample learning for domain adaptation. World Wide Web 27, 55 (2024). https://doi.org/10.1007/s11280-024-01290-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11280-024-01290-3

Keywords

Search

Navigation