Skip to main content
SpringerLink
Log in

Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks

  • Conference paper
  • First Online:
Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12624))

Included in the following conference series:

Abstract

Statistical and adversarial adaptation are currently two extensive categories of neural network architectures in unsupervised deep domain adaptation. The latter has become the new standard due to its good theoretical foundation and empirical performance. However, there are two shortcomings. First, recent studies show that these approaches focus too much on easily transferable features and thus neglect important discriminative information. Second, adversarial networks are challenging to train. We addressed the first issue by the alignment of transferable spectral properties within an adversarial model to balance the focus between the easily transferable features and the necessary discriminatory features, while at the same time limiting the learning of domain-specific semantics by relevance considerations. Second, we stabilized the discriminator networks training procedure by Spectral Normalization employing the Lipschitz continuous gradients. We provide a theoretical and empirical evaluation of our improved approach and show its effectiveness in a performance study on standard benchmark data sets against various other state of the art methods.

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

References

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

    Google Scholar 

  2. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing [review article]. IEEE Comput. Intell. Mag. 13, 55–75 (2018)

    Article  Google Scholar 

  3. 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 

  4. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 3320–3328. Curran Associates, Inc. (2014)

    Google Scholar 

  5. Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Volume 37 of Proceedings of Machine Learning Research, Lille, France, pp. 97–105. PMLR (2015)

    Google Scholar 

  6. Zellinger, W., Grubinger, T., Lughofer, E., Natschläger, T., Saminger-Platz, S.: Central moment discrepancy (CMD) for domain-invariant representation learning. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings (2017)

    Google Scholar 

  7. Chen, X., Wang, S., Long, M., Wang, J.: Transferability vs. discriminability: batch spectral penalization for adversarial domain adaptation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Volume 97 of Proceedings of Machine Learning Research, Long Beach, California, USA, pp. 1081–1090. PMLR (2019)

    Google Scholar 

  8. Ganin, Y., Lempitsky, V.S.: Unsupervised domain adaptation by backpropagation. In: Proceedings of the 32nd International Conference on Machine Learning, ICLR 2015, Lille, France, 6–11 July 2015, pp. 1180–1189 (2015)

    Google Scholar 

  9. Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014)

    Google Scholar 

  10. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79, 151–175 (2010). https://doi.org/10.1007/s10994-009-5152-4

    Article  MathSciNet  Google Scholar 

  11. Chen, X., Wang, S., Fu, B., Long, M., Wang, J.: Catastrophic forgetting meets negative transfer: batch spectral shrinkage for safe transfer learning. In: Wallach, H., et al. (eds.) Advances in Neural Information Processing Systems 32, pp. 1908–1918. Curran Associates, Inc. (2019)

    Google Scholar 

  12. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  13. 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 - Volume 70. ICML 2017, pp. 2208–2217. JMLR.org (2017)

    Google Scholar 

  14. Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Volume 80 of Proceedings of Machine Learning Research, Stockholmsmässan, Stockholm, Sweden, pp. 1989–1998. PMLR (2018)

    Google Scholar 

  15. Shen, J., Qu, Y., Zhang, W., Yu, Y.: Wasserstein distance guided representation learning for domain adaptation. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 4058–4065. AAAI Press (2018)

    Google Scholar 

  16. Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3–8 December 2018, Montréal, Canada, pp. 1647–1657 (2018)

    Google Scholar 

  17. Li, S., Liu, C.H., Xie, B., Su, L., Ding, Z., Huang, G.: Joint adversarial domain adaptation. In: Proceedings of the 27th ACM International Conference on Multimedia, New York, NY, USA, pp. 729–737. ACM (2019)

    Google Scholar 

  18. Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723–773 (2012)

    MathSciNet  MATH  Google Scholar 

  19. Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Volume 37 of Proceedings of Machine Learning Research, Lille, France, pp. 1718–1727. PMLR (2015)

    Google Scholar 

  20. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Volume 70 of Proceedings of Machine Learning Research., International Convention Centre, Sydney, Australia, pp. 214–223 PMLR (2017)

    Google Scholar 

  21. Balaji, Y., Chellappa, R., Feizi, S.: Normalized Wasserstein for mixture distributions with applications in adversarial learning and domain adaptation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6499–6507. IEEE (2019)

    Google Scholar 

  22. Zhao, L., Liu, Y.: Spectral normalization for domain adaptation. Information 11, 68 (2020)

    Article  Google Scholar 

  23. Rakshit, S., Chaudhuri, U., Banerjee, B., Chaudhuri, S.: Class consistency driven unsupervised deep adversarial domain adaptation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 657–666. IEEE (2019)

    Google Scholar 

  24. Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)

    Google Scholar 

  25. Papadopoulo, T., Lourakis, M.I.A.: Estimating the Jacobian of the singular value decomposition: theory and applications. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 554–570. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45054-8_36

    Chapter  Google Scholar 

  26. Petersen, K.B., Pedersen, M.S.: The Matrix Cookbook (2008)

    Google Scholar 

  27. Bro, R., Acar, E., Kolda, T.G.: Resolving the sign ambiguity in the singular value decomposition. J. Chemometr. 22, 135–140 (2008)

    Article  Google Scholar 

  28. Zhao, H., Combes, R.T.D., Zhang, K., Gordon, G.: On learning invariant representations for domain adaptation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Volume 97 of Proceedings of Machine Learning Research, Long Beach, California, USA, pp. 7523–7532. PMLR (2019)

    Google Scholar 

  29. He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 15, pp. 5353–5360. IEEE (2015)

    Google Scholar 

  30. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16

    Chapter  Google Scholar 

  31. Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2017-January, pp. 5385–5394. IEEE (2017)

    Google Scholar 

  32. Xu, R., Li, G., Yang, J., Lin, L.: Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Volume 2019-October, pp. 1426–1435. IEEE (2019)

    Google Scholar 

  33. Li, M., Zhai, Y.M., Luo, Y.W., Ge, P.F., Ren, C.X.: Enhanced transport distance for unsupervised domain adaptation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13936–13944 (2020)

    Google Scholar 

  34. Zhong, E., Fan, W., Yang, Q., Verscheure, O., Ren, J.: Cross validation framework to choose amongst models and datasets for transfer learning. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 547–562. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15939-8_35

    Chapter  Google Scholar 

  35. Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 137–144. MIT Press (2007)

    Google Scholar 

  36. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 164, 10 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany

    Christoph Raab, Philipp Väth, Peter Meier & Frank-Michael Schleif

Authors
  1. Christoph Raab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philipp Väth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Meier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank-Michael Schleif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Raab .

Editor information

Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 6038 KB)

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

Raab, C., Väth, P., Meier, P., Schleif, FM. (2021). Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12624. Springer, Cham. https://doi.org/10.1007/978-3-030-69535-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69535-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69534-7

  • Online ISBN: 978-3-030-69535-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation