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Interpretable Open-Set Domain Adaptation via Angular Margin Separation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Open-set Domain Adaptation (OSDA) aims to recognize classes in the target domain that are seen in the source domain while rejecting other unseen target-exclusive classes into an unknown class, which ignores the diversity of the latter and is therefore incapable of their interpretation. The recently-proposed Semantic Recovery OSDA (SR-OSDA) brings in semantic attributes and attacks the challenge via partial alignment and visual-semantic projection, marking the first step towards interpretable OSDA. Following that line, in this work, we propose a representation learning framework termed Angular Margin Separation (AMS) that unveils the power of discriminative and robust representation for both open-set domain adaptation and cross-domain semantic recovery. Our core idea is to exploit an additive angular margin with regularization for both robust feature fine-tuning and discriminative joint feature alignment, which turns out advantageous to learning an accurate and less biased visual-semantic projection. Further, we propose a post-training re-projection that boosts the performance of seen classes interpretation without deterioration on unseen classes. Verified by extensive experiments, AMS achieves a notable improvement over the existing SR-OSDA baseline, with an average 7.6% increment in semantic recovery accuracy of unseen classes in multiple transfer tasks. Our code is available at AMS.

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References

  1. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  2. Bucci, S., Borlino, F.C., Caputo, B., Tommasi, T.: Distance-based hyperspherical classification for multi-source open-set domain adaptation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1119–1128 (2022)

    Google Scholar 

  3. Bucci, S., Loghmani, M.R., Tommasi, T.: On the effectiveness of image rotation for open set domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 422–438. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_25

    Chapter  Google Scholar 

  4. Chao, W.-L., Changpinyo, S., Gong, B., Sha, F.: An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 52–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_4

    Chapter  Google Scholar 

  5. 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, pp. 3339–3348 (2018)

    Google Scholar 

  6. Choi, J., Sharma, G., Schulter, S., Huang, J.-B.: Shuffle and attend: video domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 678–695. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_40

    Chapter  Google Scholar 

  7. Csurka, G.: Domain adaptation for visual applications: a comprehensive survey. arXiv preprint arXiv:1702.05374 (2017)

  8. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  9. Du, Z., Li, J., Lu, K., Zhu, L., Huang, Z.: Learning transferrable and interpretable representations for domain generalization. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3340–3349 (2021)

    Google Scholar 

  10. Feng, Q., Kang, G., Fan, H., Yang, Y.: Attract or distract: exploit the margin of open set. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7990–7999 (2019)

    Google Scholar 

  11. 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, pp. 770–778 (2016)

    Google Scholar 

  12. Jing, M., Li, J., Zhu, L., Ding, Z., Lu, K., Yang, Y.: Balanced open set domain adaptation via centroid alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8013–8020 (2021)

    Google Scholar 

  13. Jing, T., Liu, H., Ding, Z.: Towards novel target discovery through open-set domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9322–9331 (2021)

    Google Scholar 

  14. Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3174–3183 (2017)

    Google Scholar 

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012)

    Google Scholar 

  16. Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 951–958. IEEE (2009)

    Google Scholar 

  17. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  18. Li, J., Chen, E., Ding, Z., Zhu, L., Lu, K., Shen, H.T.: Maximum density divergence for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3918–3930 (2020)

    Article  Google Scholar 

  19. Li, J., Du, Z., Zhu, L., Ding, Z., Lu, K., Shen, H.T.: Divergence-agnostic unsupervised domain adaptation by adversarial attacks. IEEE Trans. Pattern Anal. Mach. Intell. (2021)

    Google Scholar 

  20. Li, J., Jing, M., Lu, K., Ding, Z., Zhu, L., Huang, Z.: Leveraging the invariant side of generative zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7402–7411 (2019)

    Google Scholar 

  21. Li, J., Jing, M., Zhu, L., Ding, Z., Lu, K., Yang, Y.: Learning modality-invariant latent representations for generalized zero-shot learning. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1348–1356 (2020)

    Google Scholar 

  22. Li, J., Lu, K., Huang, Z., Zhu, L., Shen, H.T.: Transfer independently together: a generalized framework for domain adaptation. IEEE Trans. Cybernet. 49(6), 2144–2155 (2018)

    Article  Google Scholar 

  23. Li, X., Li, J., Zhu, L., Wang, G., Huang, Z.: Imbalanced source-free domain adaptation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3330–3339 (2021)

    Google Scholar 

  24. Li, Y., Wang, D., Hu, H., Lin, Y., Zhuang, Y.: Zero-shot recognition using dual visual-semantic mapping paths. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3279–3287 (2017)

    Google Scholar 

  25. Liu, H., Cao, Z., Long, M., Wang, J., Yang, Q.: Separate to adapt: open set domain adaptation via progressive separation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2927–2936 (2019)

    Google Scholar 

  26. Liu, S., Long, M., Wang, J., Jordan, M.I.: Generalized zero-shot learning with deep calibration network. Advances in Neural Information Processing Systems 31 (2018)

    Google Scholar 

  27. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)

    Google Scholar 

  28. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: generalizing to new classes at near-zero cost. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2624–2637 (2013)

    Article  Google Scholar 

  29. Miller, D., Sunderhauf, N., Milford, M., Dayoub, F.: Class anchor clustering: a loss for distance-based open set recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3570–3578 (2021)

    Google Scholar 

  30. Narayan, S., Gupta, A., Khan, F.S., Snoek, C.G.M., Shao, L.: Latent embedding feedback and discriminative features for zero-shot classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 479–495. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_29

    Chapter  Google Scholar 

  31. Panareda Busto, P., Gall, J.: Open set domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 754–763 (2017)

    Google Scholar 

  32. 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, pp. 1406–1415 (2019)

    Google Scholar 

  33. Pourpanah, F., et al.: A review of generalized zero-shot learning methods. arXiv preprint arXiv:2011.08641 (2020)

  34. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

  35. Rodríguez, P., Laradji, I., Drouin, A., Lacoste, A.: Embedding propagation: smoother manifold for few-shot classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 121–138. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_8

    Chapter  Google Scholar 

  36. Romera-Paredes, B., Torr, P.: An embarrassingly simple approach to zero-shot learning. In: International Conference on Machine Learning, pp. 2152–2161. PMLR (2015)

    Google Scholar 

  37. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  38. Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 156–171. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_10

    Chapter  Google Scholar 

  39. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  40. Song, J., Shen, C., Yang, Y., Liu, Y., Song, M.: Transductive unbiased embedding for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1024–1033 (2018)

    Google Scholar 

  41. Su, H., Li, J., Chen, Z., Zhu, L., Lu, K.: Distinguishing unseen from seen for generalized zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7885–7894 (2022)

    Google Scholar 

  42. Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)

    Article  Google Scholar 

  43. Wang, X., Ye, Y., Gupta, A.: Zero-shot recognition via semantic embeddings and knowledge graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6857–6866 (2018)

    Google Scholar 

  44. Wang, Z., Gou, Y., Li, J., Zhang, Y., Yang, Y.: Region semantically aligned network for zero-shot learning. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2080–2090 (2021)

    Google Scholar 

  45. Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251–2265 (2018)

    Article  Google Scholar 

  46. Xu, X., et al.: Matrix tri-factorization with manifold regularizations for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3798–3807 (2017)

    Google Scholar 

  47. Yang, H.M., Zhang, X.Y., Yin, F., Liu, C.L.: Robust classification with convolutional prototype learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3474–3482 (2018)

    Google Scholar 

  48. Ye, M., Guo, Y.: Zero-shot classification with discriminative semantic representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7140–7148 (2017)

    Google Scholar 

  49. You, F., Li, J., Zhu, L., Chen, Z., Huang, Z.: Domain adaptive semantic segmentation without source data. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3293–3302 (2021)

    Google Scholar 

  50. Zhang, L., Xiang, T., Gong, S.: Learning a deep embedding model for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2021–2030 (2017)

    Google Scholar 

  51. Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: Learning with local and global consistency. Advances in Neural Information Processing Systems 16 (2003)

    Google Scholar 

  52. Zhu, Y., Xie, J., Liu, B., Elgammal, A.: Learning feature-to-feature translator by alternating back-propagation for generative zero-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9844–9854 (2019)

    Google Scholar 

  53. Zhuo, J., Wang, S., Cui, S., Huang, Q.: Unsupervised open domain recognition by semantic discrepancy minimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 750–759 (2019)

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62176042 and 62073059, in part by CCF-Baidu Open Fund (NO. 2021PP15002000), in part by CCF-Tencent Open Fund (NO. RAGR20210107), and in part by Guangdong Basic and Applied Basic Research Foundation (No. 2021B1515140013).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Xinhao Li, Jingjing Li, Zhekai Du & Wen Li

  2. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China

    Jingjing Li

  3. Shandong Normal University, Jinan, China

    Lei Zhu

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  1. Xinhao Li
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Correspondence to Jingjing Li .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Li, X., Li, J., Du, Z., Zhu, L., Li, W. (2022). Interpretable Open-Set Domain Adaptation via Angular Margin Separation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13694. Springer, Cham. https://doi.org/10.1007/978-3-031-19830-4_1

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