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Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12359)

Abstract

Although unsupervised domain adaptation methods have been widely adopted across several computer vision tasks, it is more desirable if we can exploit a few labeled data from new domains encountered in a real application. The novel setting of the semi-supervised domain adaptation (SSDA) problem shares the challenges with the domain adaptation problem and the semi-supervised learning problem. However, a recent study shows that conventional domain adaptation and semi-supervised learning methods often result in less effective or negative transfer in the SSDA problem. In order to interpret the observation and address the SSDA problem, in this paper, we raise the intra-domain discrepancy issue within the target domain, which has never been discussed so far. Then, we demonstrate that addressing the intra-domain discrepancy leads to the ultimate goal of the SSDA problem. We propose an SSDA framework that aims to align features via alleviation of the intra-domain discrepancy. Our framework mainly consists of three schemes, i.e., attraction, perturbation, and exploration. First, the attraction scheme globally minimizes the intra-domain discrepancy within the target domain. Second, we demonstrate the incompatibility of the conventional adversarial perturbation methods with SSDA. Then, we present a domain adaptive adversarial perturbation scheme, which perturbs the given target samples in a way that reduces the intra-domain discrepancy. Finally, the exploration scheme locally aligns features in a class-wise manner complementary to the attraction scheme by selectively aligning unlabeled target features complementary to the perturbation scheme. We conduct extensive experiments on domain adaptation benchmark datasets such as DomainNet, Office-Home, and Office. Our method achieves state-of-the-art performances on all datasets.

Keywords

Domain adaptation Semi-supervised learning 

Supplementary material

504468_1_En_35_MOESM1_ESM.pdf (126 kb)
Supplementary material 1 (pdf 126 KB)

References

  1. 1.
    Ao, S., Li, X., Ling, C.X.: Fast generalized distillation for semi-supervised domain adaptation. In: AAAI (2017)Google Scholar
  2. 2.
    Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C., Huang, J.B.: A closer look at few-shot classification. In: International Conference on Learning Representations (ICLR) (2019)Google Scholar
  3. 3.
    Chen, Y., Zhu, X., Gong, S.: Semi-supervised deep learning with memory. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)Google Scholar
  4. 4.
    Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  5. 5.
    Cicek, S., Fawzi, A., Soatto, S.: SaaS: speed as a supervisor for semi-supervised learning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)Google Scholar
  6. 6.
    Donahue, J., Hoffman, J., Rodner, E., Saenko, K., Darrell, T.: Semi-supervised domain adaptation with instance constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar
  7. 7.
    Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Proceedings of the International Conference on Machine Learning (ICML) (2015)Google Scholar
  8. 8.
    Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems (NeurIPS) (2005)Google Scholar
  9. 9.
    Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. Journal of Machine Learning Research (JMLR) (2012)Google Scholar
  10. 10.
    Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A.A., Darrell, T.: Cycada: cycle-consistent adversarial domain adaptation. In: Proceedings of the International Conference on Machine Learning (ICML) (2018)Google Scholar
  11. 11.
    Hong, W., Wang, Z., Yang, M., Yuan, J.: Conditional generative adversarial network for structured domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  12. 12.
    Hu, L., Kan, M., Shan, S., Chen, X.: Duplex generative adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  13. 13.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  14. 14.
    Kim, T., Jeong, M., Kim, S., Choi, S., Kim, C.: Diversify and match: A domain adaptive representation learning paradigm for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  15. 15.
    Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: International Conference on Learning Representations (ICLR) (2017)Google Scholar
  16. 16.
    Lee, S., Kim, D., Kim, N., Jeong, S.G.: Drop to adapt: Learning discriminative features for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)Google Scholar
  17. 17.
    Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: Proceedings of the International Conference on Machine Learning (ICML) (2015)Google Scholar
  18. 18.
    Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)Google Scholar
  19. 19.
    Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)Google Scholar
  20. 20.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research (JMLR) (2008)Google Scholar
  21. 21.
    Miyato, T., Maeda, S.i., Ishii, S., Koyama, M.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2018)Google Scholar
  22. 22.
    Nam, H., Lee, H., Park, J., Yoon, W., Yoo, D.: Reducing domain gap via style-agnostic networks (2019)Google Scholar
  23. 23.
    Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)Google Scholar
  24. 24.
    Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)Google Scholar
  25. 25.
    Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Proceedings of the European Conference on Computer Vision (ECCV) (2010)Google Scholar
  26. 26.
    Saito, K., Kim, D., Sclaroff, S., Darrell, T., Saenko, K.: Semi-supervised domain adaptation via minimax entropy. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)Google Scholar
  27. 27.
    Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Adversarial dropout regularization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)Google Scholar
  28. 28.
    Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  29. 29.
    Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in neural information processing systems (NeurIPS) (2017)Google Scholar
  30. 30.
    Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  31. 31.
    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 (CVPR) (2017)Google Scholar
  32. 32.
    Wang, Q., Li, W., Gool, L.V.: Semi-supervised learning by augmented distribution alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)Google Scholar
  33. 33.
    Yao, T., Yingwei Pan, Ngo, C., Houqiang Li, Tao Mei: Semi-supervised domain adaptation with subspace learning for visual recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  34. 34.
    Zheng, C., Cham, T.J., Cai, J.: T2Net: Synthetic-to-realistic translation for solving single-image depth estimation tasks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)Google Scholar
  35. 35.
    Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Korea Advanced Institute of Science and TechnologyDaejeonSouth Korea

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