Skip to main content
SpringerLink
Log in

Adversarial Continuous Learning in Unsupervised Domain Adaptation

  • Conference paper
  • First Online:
Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12662))

Included in the following conference series:

Abstract

Domain adaptation has emerged as a crucial technique to address the problem of domain shift, which exists when applying an existing model to a new population of data. Adversarial learning has made impressive progress in learning a domain invariant representation via building bridges between two domains. However, existing adversarial learning methods tend to only employ a domain discriminator or generate adversarial examples that affect the original domain distribution. Moreover, little work has considered confident continuous learning using an existing source classifier for domain adaptation. In this paper, we develop adversarial continuous learning in a unified deep architecture. We also propose a novel correlated loss to minimize the discrepancy between the source and target domain. Our model increases robustness by incorporating high-confidence samples from the target domain. The transfer loss jointly considers the original source image and transfer examples in the target domain. Extensive experiments demonstrate significant improvements in classification accuracy over the state of the art.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Source code is available at https://github.com/YoushanZhang/Transfer-Learning/tree/main/Code/Deep/ACDA.

References

  1. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Machine Learn. 79(1), 151–175 (2009). https://doi.org/10.1007/s10994-009-5152-4

    Article  MathSciNet  Google Scholar 

  2. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)

    MathSciNet  Google Scholar 

  3. Ghifary, M., Kleijn, W.B., Zhang, M.: Domain adaptive neural networks for object recognition. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 898–904. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13560-1_76

    Chapter  Google Scholar 

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

    Google Scholar 

  5. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  6. Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 999–1006. IEEE (2011)

    Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  8. Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4893–4902 (2019)

    Google Scholar 

  9. Liu, H., Long, M., Wang, J., Jordan, M.: Transferable adversarial training: a general approach to adapting deep classifiers. In: International Conference on Machine Learning, pp. 4013–4022 (2019)

    Google Scholar 

  10. Long, J., Wang, M., Ding, G., Pan, S.J., Philip, S.Y.: Adaptation regularization: a general framework for transfer learning. IEEE Trans. Knowl. Data Eng. 26(5), 1076–1089 (2013)

    Article  Google Scholar 

  11. Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. arXiv preprint arXiv:1502.02791 (2015)

  12. Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 1647–1657 (2018)

    Google Scholar 

  13. Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)

    Google Scholar 

  14. Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems, pp. 136–144 (2016)

    Google Scholar 

  15. Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2208–2217. JMLR.org (2017)

    Google Scholar 

  16. Miyato, T., Maeda, S., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)

    Article  Google Scholar 

  17. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  18. Rahman, M.M., Fookes, C., Baktashmotlagh, M., Sridharan, S.: On Minimum Discrepancy Estimation for Deep Domain Adaptation. In: Singh, R., Vatsa, M., Patel, V.M., Ratha, N. (eds.) Domain Adaptation for Visual Understanding, pp. 81–94. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-30671-7_6

    Chapter  Google Scholar 

  19. K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting visual category models to new domains. In Proceedings of the European Conference on Computer Vision, pages 213–226. Springer, 2010

    Google Scholar 

  20. Shu, R., Bui, H.H., Narui, H., Ermon, S.: A dirt-t approach to unsupervised domain adaptation. arXiv preprint arXiv:1802.08735 (2018)

  21. Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_35

    Chapter  Google Scholar 

  22. Tzeng, E., Hoffman, J., Saenko, K., Darrell, K.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)

    Google Scholar 

  23. Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)

  24. 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)

    Google Scholar 

  25. Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM International Conference on Multimedia, MM 2018, pp. 402–410 (2018)

    Google Scholar 

  26. Wang, X., Li, L., Ye, W., Long, M., Wang, J.: Transferable attention for domain adaptation. Proc. AAAI Conf. Artif. Intell. 33, 5345–5352 (2019)

    Google Scholar 

  27. Zhang, J., Li, W., Ogunbona, P.: Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1859–1867 (2017)

    Google Scholar 

  28. Zhang, W., Ouyang, W., Li, W., Xu, D.: Collaborative and adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3801–3809 (2018)

    Google Scholar 

  29. Zhang, Y., Allem, J.P., Unger, J.B., Cruz, T.B.: Automated identification of hookahs (waterpipes) on instagram: an application in feature extraction using convolutional neural network and support vector machine classification. J. Med. Internet Res. 20(11), e10513 (2018)

    Article  Google Scholar 

  30. Zhang, Y., Davison, B.D.: Modified distribution alignment for domain adaptation with pre-trained Inception ResNet. arXiv preprint arXiv:1904.02322 (2019)

  31. Zhang, Y., Davison, B.D.: Adversarial consistent learning on partial domain adaptation of PlantCLEF 2020 challenge. In: CLEF working notes 2020, CLEF: Conference and Labs of the Evaluation Forum (2020)

    Google Scholar 

  32. Zhang, Y., Davison, B.D.: Domain adaptation for object recognition using subspace sampling demons. Multimedia Tools Appl. 1–20 (2020). https://doi.org/10.1007/s11042-020-09336-0

  33. Zhang, Y., Davison, B.D.: Impact of ImageNet model selection on domain adaptation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops, pp. 173–182 (2020)

    Google Scholar 

  34. Zhang, Y., Tang, H., Jia, K., Tan, M.: Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5031–5040 (2019)

    Google Scholar 

  35. Zhang, Y., Xie, S., Davison, B.D.: Transductive learning via improved geodesic sampling. In: Proceedings of the 30th British Machine Vision Conference (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA

    Youshan Zhang & Brian D. Davison

Authors
  1. Youshan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian D. Davison
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youshan Zhang .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, Universita di Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Davison, B.D. (2021). Adversarial Continuous Learning in Unsupervised Domain Adaptation. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68790-8_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68789-2

  • Online ISBN: 978-3-030-68790-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation