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Cross-domain feature enhancement for unsupervised domain adaptation

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Abstract

Till the present, the domain adaptation has been widely researched by transferring the knowledge from a labeled source domain to an unlabeled target domain. Adversarial adaptation methods have achieved great success, learning domain-invariant representations with category semantic information. Although domain-invariant representation is obtained, domain-specific variation is suppressed, which may distort the original feature distribution. In this paper, we propose a novel method called Cross-domain Feature Enhancement Domain Adaptation (CFEDA) which fills in the domain discrepancy to address the challenge of original domain feature information damage. Specifically, by leveraging the cross-domain and intra-domain prototype representations that are extracted through clustering, the features of both source and target domains can be enhanced. As a result, similar source domain and similar target domain features can be produced in the feature space to fill in the domain discrepancy. Since the target domain feature is unlabeled and can not be directly adopted for training, we exploit a feature consistency loss on it. Moreover, extensive experiments are conducted to demonstrate that CFEDA achieves significant performance improvements.

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References

  1. Adams N (2010) Dataset shift in machine learning by j. quiñonero-candela; m. sugiyama; a. schwaighofer; n. d. lawrence. J R Statal Soc Ser A 173(1):274

    Article  Google Scholar 

  2. Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359

    Article  Google Scholar 

  3. Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning. PMLR, pp 1180–1189

  4. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inform Process Syst 27

  5. Zhao X, Wang S (2019) Adversarial learning and interpolation consistency for unsupervised domain adaptation. IEEE Access 7:170448–170456

    Article  Google Scholar 

  6. Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Machine Learn 79(1):151–175

    Article  MathSciNet  MATH  Google Scholar 

  7. Peng X, Bai Q, Xia X, Huang Z, Saenko K, Wang B (2019) Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1406–1415

  8. Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S (2017) Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5018–5027

  9. Peng X, Usman B, Kaushik N, Hoffman J, Wang D, Saenko K (2017) Visda: The visual domain adaptation challenge. arXiv:1710.06924

  10. 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

  11. Gretton A, Borgwardt K, Rasch M, Schölkopf B, Smola A (2006) A kernel method for the two-sample-problem. Adv Neural Inform Process Syst 19:513–520

    MATH  Google Scholar 

  12. Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola A (2012) A kernel two-sample test. J Machine Learn Res 13(1):723–773

    MathSciNet  MATH  Google Scholar 

  13. Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  14. 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

  15. 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

  16. Long M, Cao Z, Wang J, Jordan MI (2017) Conditional adversarial domain adaptation. arXiv:1705.10667

  17. 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

  18. Pei Z, Cao Z, Long M, Wang J (2018) Multi-adversarial domain adaptation. In: Thirty-second AAAI conference on artificial intelligence

  19. Luo Y, Zheng L, Guan T, Yu J, Yang Y (2019) Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2507–2516

  20. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3722–3731

  21. Sankaranarayanan S, Balaji Y, Castillo CD, Chellappa R (2018) Generate to adapt: Aligning domains using generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8503–8512

  22. 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. PMLR, pp 1989–1998

  23. Kumar N, Sukavanam N (2020) An improved cnn framework for detecting and tracking human body in unconstraint environment. Knowledge-Based Systems 193:105198

    Article  Google Scholar 

  24. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  25. Zhou W, Guo Q, Lei J, Yu L, Hwang J-N (2021) Irfr-net: Interactive recursive feature-reshaping network for detecting salient objects in rgb-d images. IEEE Transactions on Neural Networks and Learning Systems

  26. Zhang D, Ye M, Liu Y, Xiong L, Zhou L (2022) Multi-source unsupervised domain adaptation for object detection. Inform Fusion 78:138–148

    Article  Google Scholar 

  27. Zhou W, Liu J, Lei J, Yu L, Hwang J-N (2021) Gmnet: graded-feature multilabel-learning network for rgb-thermal urban scene semantic segmentation. IEEE Trans Image Process 30:7790–7802

    Article  Google Scholar 

  28. He C, Wang S, Kang H, Zheng L, Tan T, Fan X (2021) Adversarial domain adaptation network for tumor image diagnosis. Int J Approx Reason 135:38–52

    Article  MathSciNet  MATH  Google Scholar 

  29. Snell J, Swersky K, Zemel RS (2017) Prototypical networks for few-shot learning. arXiv:1703.05175

  30. Lee D-H et al (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, vol 3. ICML, p 896

  31. Luo Z, Zou Y, Hoffman J, Fei-Fei L (2017) Label efficient learning of transferable representations across domains and tasks. arXiv:1712.00123

  32. Venkateswara H, Panchanathan S (2020) Domain adaptation in computer vision with deep learning

  33. Motiian S, Piccirilli M, Adjeroh DA, Doretto G (2017) Unified deep supervised domain adaptation and generalization. In: Proceedings of the IEEE international conference on computer vision, pp 5715–5725

  34. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision. Springer, pp 740–755

  35. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    MathSciNet  MATH  Google Scholar 

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

  37. Cao Z, You K, Long M, Wang J, Yang Q (2019) Learning to transfer examples for partial domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2985–2994

  38. Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning. PMLR, pp 2208–2217

  39. Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723–3732

  40. French G, Mackiewicz M, Fisher M (2017) Self-ensembling for visual domain adaptation. arXiv:1706.05208

  41. Zhang Y, Tang H, Jia K, Tan M (2019) Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5031–5040

  42. Zhang Y, Liu T, Long M, Jordan M (2019) Bridging theory and algorithm for domain adaptation. In: International conference on machine learning. PMLR, pp 7404–7413

  43. Smith LN, Topin N (2019) Super-convergence: Very fast training of neural networks using large learning rates. In: Artificial intelligence and machine learning for multi-domain operations applications, vol 11006. International Society for Optics and Photonics, p 1100612

  44. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  45. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. nips-w. In: Proceedings of the 31st conference on neural information processing systems (NIPS 2017), Long Beach, CA, USA, pp 4–9

  46. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1097–1105

    Google Scholar 

  47. Du Z, Li J, Su H, Zhu L, Lu K (2021) Cross-domain gradient discrepancy minimization for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3937–3946

  48. Li S, Zhang J, Ma W, Liu CH, Li W (2021) Dynamic domain adaptation for efficient inference. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7832–7841

  49. Cui S, Wang S, Zhuo J, Li L, Huang Q, Tian Q (2020) Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3941–3950

  50. Li J, Chen E, Ding Z, Zhu L, Lu K, Shen HT (2020) Maximum density divergence for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence

  51. Cui S, Wang S, Zhuo J, Su C, Huang Q, Tian Q (2020) Gradually vanishing bridge for adversarial domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12455–12464

  52. Han Z, Sun H, Yin Y (2021) Learning transferable parameters for unsupervised domain adaptation. arXiv:2108.06129

  53. Sun H, Lin L, Liu N, Zhou H (2021) Robust ensembling network for unsupervised domain adaptation. In: Pacific Rim international conference on artificial intelligence. Springer, pp 530–543

  54. Yang G, Ding M, Zhang Y (2021) Bi-directional class-wise adversaries for unsupervised domain adaptation. Appl Intell, 1–17

  55. Chen X, Wang S, Long M, Wang J (2019) Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In: International conference on machine learning. PMLR, pp 1081–1090

  56. Xiao N, Zhang L (2021) Dynamic weighted learning for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 15242–15251

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Acknowledgements

This work is supported by the Innovation Capacity Construction Project of Jilin Province Development and Reform Commission (2021FGWCXNLJSSZ10), the National Key Research and Development Program of China (No. 2020YFA0714103) and the Science & Technology Development Project of Jilin Province, China (20190302117GX) and Graduate Innovation Fund of Jilin University (101832020CX179).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, Jilin, China

    Long Sifan, Wang Shengsheng, Zhao Xin, Fu Zihao & Wang Bilin

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 2699 Qianjin Street, Changchun, 130012, Jilin, China

    Long Sifan, Wang Shengsheng, Zhao Xin, Fu Zihao & Wang Bilin

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  1. Long Sifan
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Correspondence to Wang Shengsheng.

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Sifan, L., Shengsheng, W., Xin, Z. et al. Cross-domain feature enhancement for unsupervised domain adaptation. Appl Intell 52, 17326–17340 (2022). https://doi.org/10.1007/s10489-022-03306-9

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