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Asymmetric alignment joint consistent regularization for multi-source domain adaptation

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Abstract

Most existing methods of domain adaptation are proposed for single-source settings, where the source data come from a single domain. However, in practice, the available data generally come from multiple domains with different distributions. In this paper, we propose asymmetric alignment joint consistent regularization (AACR) for multi-source domain adaptation. As for asymmetric alignment, we propose to learn asymmetric projections, explicitly treating each domain differently to avoid the loss of shared information. Data from different domains are encoded into a common subspace by these asymmetric projections, where the encoded features are further aligned to promote knowledge transfer. Further, we propose consistent regularization to better learn shared information and filter out domain-specific information. We formulate the two parts into a unified framework, and derive its global optimal solution. Comprehensive experiments are conducted to evaluate AACR, and the results verify the effectiveness and robustness of our method.

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References

  1. Bartels RH, Stewart GW (1972) Solution of the matrix equation ax+ xb= c [f4]. Commun ACM 15(9):820–826

    Article  Google Scholar 

  2. Bruzzone L, Marconcini M (2009) Domain adaptation problems: A dasvm classification technique and a circular validation strategy. IEEE transactions on pattern analysis and machine intelligence 32(5):770–787

    Article  Google Scholar 

  3. Chen Y-C, Zhu X, Zheng W-S, Lai J-H (2017) Person re-identification by camera correlation aware feature augmentation. IEEE transactions on pattern analysis and machine intelligence 40(2):392–408

    Article  Google Scholar 

  4. Chen Y, Liu L, Tao J, Xia R, Zhang Q, Yang K, Xiong J, Chen X (2020) The improved image inpainting algorithm via encoder and similarity constraint, Vis Comput, 1–15

  5. Chen Y, Tao J, Liu L, Xiong J, Xia R, Xie J, Zhang Q, Yang K (2020) Research of improving semantic image segmentation based on a feature fusion model, journal of ambient intelligence and humanized computing

  6. Chen Y, Tao J, Zhang Q, Yang K, Chen X, Xiong J, Xia R, Xie J (2020) Saliency detection via the improved hierarchical principal component analysis method, Wirel Commun Mob Comput, 2020

  7. Chen Y, Wang J, Chen X, Sangaiah AK, Yang K, Cao Z (2019) Image super-resolution algorithm based on dual-channel convolutional neural networks. Appl Sci 9(11):2316

    Article  Google Scholar 

  8. Chen Y, Wang J, Chen X, Zhu M, Yang K, Wang Z, Xia R (2019) Single-image super-resolution algorithm based on structural self-similarity and deformation block features. IEEE Access 7:58791–58801

    Article  Google Scholar 

  9. Chen Y, Wang J, Liu S, Chen X, Xiong J, Xie J, Yang K (2019) Multiscale fast correlation filtering tracking algorithm based on a feature fusion model, concurrency and computation: practice and experience, e5533

  10. Chen Y, Wang J, Xia R, Zhang Q, Cao Z, Yang K (2019) The visual object tracking algorithm research based on adaptive combination kernel. Journal of Ambient Intelligence and Humanized Computing 10(12):4855–4867

    Article  Google Scholar 

  11. Chen Y, Xiong J, Xu W, Zuo J (2019) A novel online incremental and decremental learning algorithm based on variable support vector machine. Clust Comput 22(3):7435–7445

    Article  Google Scholar 

  12. Chen Y, Xu W, Zuo J, Yang K (2019) The fire recognition algorithm using dynamic feature fusion and iv-svm classifier. Clust Comput 22(3):7665–7675

    Article  Google Scholar 

  13. Chung FRK, Graham FC (1997) Spectral graph theory, american mathematical soc., 92

  14. Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE international conference on computer vision, pp 2960–2967

  15. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2066–2073

  16. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset

  17. Gu Q, Li Z, Han J (2011) Joint feature selection and subspace learning. In: Twenty-second international joint conference on artificial intelligence, pp 1294–1299

  18. Hoffman J, Kulis B, Darrell T, Saenko K (2012) Discovering latent domains for multisource domain adaptation. In: European Conference on Computer Vision, Springer, pp 702–715

  19. Hoffman J, Tzeng E, Park T, Zhu J-Y, Isola P, Saenko K, Efros A, Darrell T (2018) Cycada: Cycle-consistent adversarial domain adaptation. In: International conference on machine learning, pp 1989–1998

  20. Huang J, Zhou Z (2019) Transfer metric learning for unsupervised domain adaptation. IET Image Process 13(5):804–810

    Article  Google Scholar 

  21. Jiang J, Zhai C (2007) Instance weighting for domain adaptation in nlp. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 264–271

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

  23. Li J, Lu K, Huang Z, Zhu L, Shen HT (2018) Heterogeneous domain adaptation through progressive alignment. IEEE transactions on neural networks and learning systems 30(5):1381–1391

    Article  MathSciNet  Google Scholar 

  24. Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: A data perspective. ACM Computing Surveys (CSUR) 50(6):94

    Article  Google Scholar 

  25. Liao X, Yin J, Chen M, Qin Z (2020) Adaptive payload distribution in multiple images steganography based on image texture features, ieee transactions on dependable and secure computing

  26. Liu J, Li J, Lu K (2018) Coupled local–global adaptation for multi-source transfer learning. Neurocomputing 275:247–254

    Article  Google Scholar 

  27. Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, PMLR, pp 97–105

  28. Long M, Wang J, Ding G, Sun J, Yu PS (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2200–2207

  29. Long M, Wang J, Ding G, Sun J, Yu PS (2014) Transfer joint matching for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1410–1417

  30. Lu X, Ma C, Ni B, Yang X, Reid I, Yang M-H (2018) Deep regression tracking with shrinkage loss. In: Proceedings of the European conference on computer vision (ECCV), pp 353–369

  31. Lu X, Wang W, Shen J, Tai Y-W, Crandall DJ, Hoi SCH (2020) Learning video object segmentation from unlabeled videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8960–8970

  32. Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135

    Article  Google Scholar 

  33. Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of machine learning research 9:2579–2605

    MATH  Google Scholar 

  34. Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22 (2):199–210

    Article  Google Scholar 

  35. Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22(10):1345–1359

    Article  Google Scholar 

  36. Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: A survey of recent advances. IEEE signal processing magazine 32(3):53–69

    Article  Google Scholar 

  37. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision, Springer, pp 213–226

  38. Shen J, Qu Y, Zhang W, Yu Y (2017) Wasserstein distance guided representation learning for domain adaptation. arXiv:1707.01217

  39. Si S, Tao D, Geng B (2009) Bregman divergence-based regularization for transfer subspace learning. IEEE Trans Knowl Data Eng 22(7):929–942

    Article  Google Scholar 

  40. Sim T, Baker S, Bsat M (2002) The cmu pose, illumination, and expression (pie) database. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, IEEE, pp 53–58

  41. Torralba A, Efros AA, et al. (2011) Unbiased look at dataset bias. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1521–1528

  42. Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167–7176

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

  44. Yan S, Xu D, Zhang B, Zhang H-J, Yang Q, Lin S (2006) Graph embedding and extensions: A general framework for dimensionality reduction. IEEE transactions on pattern analysis and machine intelligence 29(1):40–51

    Article  Google Scholar 

  45. Yu F, Liu L, He B, Huang Y, Shi C, Cai S, Song Y, Du S, Wan Q (2019) Analysis and fpga realization of a novel 5d hyperchaotic four-wing memristive system, active control synchronization, and secure communication application, Complexity, 2019

  46. Yu F, Liu L, Xiao L, Li K, Cai S (2019) A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function. Neurocomputing 350:108–116

    Article  Google Scholar 

  47. Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded r-cnn with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754

    Article  Google Scholar 

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

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

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Acknowledgements

The work is supported by National Key R&D Program of China (2018YFC0309400), National Natural Science Foundation of China (61871188), Guangzhou city science and technology research projects(201902020008).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, GuangZhou, China

    Junyuan Shang, Chang Niu, Zhiheng Zhou & Junchu Huang

  2. China Information and Communication Research Institute, Guangzhou, People’s Republic of China

    Zhiwei Yang

  3. School of Computer & Artificial Intelligence, Lanzhou Institute of Technology, Lanzhou, People’s Republic of China

    Xiangwei Li

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  1. Junyuan Shang
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  2. Chang Niu
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  3. Zhiheng Zhou
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Correspondence to Zhiheng Zhou.

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Shang, J., Niu, C., Zhou, Z. et al. Asymmetric alignment joint consistent regularization for multi-source domain adaptation. Multimed Tools Appl 80, 6041–6064 (2021). https://doi.org/10.1007/s11042-020-09883-6

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  • DOI: https://doi.org/10.1007/s11042-020-09883-6

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