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- Tuo Xu VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
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- Bing Han VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
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- Jie Li VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
VIPSL Laboratory, School of Electronic Engineering, Xidian University, Xian, Shannxi, China
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- Yuefan Du MCI Laboratory, School of Aerospace Science and Technology, Xidian University, Xian, Shannxi, China
MCI Laboratory, School of Aerospace Science and Technology, Xidian University, Xian, Shannxi, China
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Image and Vision ComputingVolume 141Issue CJan 2024https://doi.org/10.1016/j.imavis.2023.104887
Published:17 April 2024Publication History
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Image and Vision Computing
Volume 141, Issue C
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Abstract
Abstract
Domain adaptation has found extensive applications in diverse fields, including transfer learning, deep learning, and image processing, to effectively address the challenge of mismatched data distributions. Nevertheless, persistent challenges such as limited transfer data and the occurrence of negative transfer continue to exist. These issues not only increase the complexity of model learning but also affect the accuracy of the transfer results. To tackle these challenges, this paper introduces a novel approach called the Related Weighted Subdomain Adaptive Network (RWSAN). This network constructs related subdomain clusters in the source domain to encompass a wider spectrum of associated information. It aims to align target domain subdomains with the related subdomain clusters in the source domain as closely as possible, thus increasing the number of transferable samples from the source domain and solving insufficient transferable samples in some adaptations. To address the issue of negative transfer arising from irrelevant subdomains in the source domain during the transfer process, a related weighted maximum mean discrepancy method is introduced. This method optimizes transfer weights, minimizes the feature distribution disparity between the target domain and the source domain, and mitigates the negative impact of irrelevant subdomains on the target domain, thereby enhancing the accuracy of domain adaptation during transfer. To evaluate the proposed RWSAN method, comprehensive performance tests were conducted using widely used red-green-blue (RGB) and hyperspectral image (HIS) image datasets. The results demonstrate that the RWSAN method effectively resolves insufficient transfer data and negative transfer, proving its high reliability. Additionally, it exhibits excellent performance in error analysis, network convergence analysis, data visualization, and distribution difference.
Highlights
• | New Related Weighted Subdomain Adaptive Network (RWSAN) proposed. | ||||
• | RWSAN constructs related subdomain clusters in source domain. | ||||
• | RWSAN addresses issue of limited transfer data. | ||||
• | Method proposed to minimize feature distribution disparity. | ||||
• | Mitigates negative transfer from irrelevant subdomains. |
References
- [1] He K.M., Zhang X.Y., Ren S.Q., et al.,
Deep residual learning for image recognition[C] , in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, Piscataway, 2016, pp. 770–778.Google Scholar - [2] Singh D.K., Hoskere V., Post disaster damage assessment using ultra-high-resolution aerial imagery with semi-supervised transformers, Sensors 23 (19) (2023 Oct 3) 8235.Google Scholar
- [3] Valente N.A., Mao Z., Niezrecki C., Holistically nested edge detection and particle filtering for subtle vibration extraction, Mech. Syst. Signal Process. 204 (2023 Dec 1).Google Scholar
- [4] Chai S., Wang S., Liu C., Liu X., Liu T., Yang R., A visual measurement algorithm for vibration displacement of rotating body using semantic segmentation network, Expert Syst. Appl. 237 (2024 Mar 1).Google Scholar
- [5] Yağ İ., Altan A., Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments, Biology. 11 (12) (2022 Nov 29) 1732.Google Scholar
- [6] Özçelik Y.B., Altan A., Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long short-term memory, Fract. Fract. 7 (8) (2023 Aug 3) 598.Google Scholar
- [7] Sezer A., Altan A., Detection of solder paste defects with an optimization-based deep learning model using image processing techniques, Solder. Surf. Mount Technol. 33 (5) (2021 Oct 19) 291–298.Google Scholar
- [8] Pan S.J., Yang Q., A survey on transfer learning[J], IEEE Trans. Knowl. Data Eng. 22 (10) (2010) 1345–1359.Google Scholar
- [9] Wang M., Deng W.H., Deep visual domain adaptation: a survey[J], Neurocomputing 312 (2018) 135–153.Google Scholar
- [10] Jialin Pan S., Yang Q., A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (10) (Oct. 2010) 1345–1359.Google Scholar
- [11] Zhuang F., Cheng X., Luo P., Pan S.J., He Q.,
Supervised representa-tion learning: Transfer learning with deep autoencoders , in: Proc. IJCAI, 2015, pp. 4119–4125.Google Scholar - [12] Zhuang F., et al., A comprehensive survey on transfer learning, 2019, arXiv:1911.02685.Google Scholar
- [13] Long M., Cao Z., Wang J., Jordan M.I.,
Conditional adversarial domain adaptation , in: Proc. NIPS, 2018, pp. 1647–1657.Google Scholar - [14] Pei Z., Cao Z., Long M., Wang J.,
Multi-adversarial domain adaptation , in: Proc. AAAI, 2018, pp. 3934–3941.Google Scholar - [15] Kumar A., et al.,
Co-regularized alignment for unsupervised domain adaptation , in: Proc. NIPS, 2018, pp. 9367–9378.Google Scholar - [16] Wang J., Chen Y., Hu L., Peng X., Yu P.S.,
Stratified transfer learning for cross-domain activity recognition , in: Proc. IEEE Int. Conf. Pervas. Comput. Commun. (PerCom), Mar. 2018, pp. 1–10.Google Scholar - [17] Xie S., Zheng Z., Chen L., Chen C.,
Learning semantic representa-tions for unsupervised domain adaptation , in: Proc. ICML, 2018, pp. 5419–5428.Google Scholar - [18] Wang J., Chen Y., Yu H., Huang M., Yang Q.,
Easy transfer learning by exploiting intra-domain structures , in: Proc. IEEE Int. Conf. Multime-dia Expo (ICME), Jul. 2019, pp. 1210–1215.Google Scholar - [19] Long M., Cao Y., Wang J., Jordan M.,
Learning transferable features with deep adaptation networks , in: Proc. ICML, 2015, pp. 97–105.Google Scholar - [20] Sun B., Saenko K.,
Deep CORAL: Correlation alignment for deep domain adaptation , in: Proc. ECCV, 2016, pp. 443–450.Google Scholar - [21] Ganin Y., et al., Domain-adversarial training of neural networks, J. Mach. Learn. Res. 17 (1) (2016) 2030–2096.Google Scholar
- [22] Ganin Y., Lempitsky V.,
Unsupervised domain adaptation by back-propagation[C] , in: Proceedings of the 32nd International Conference on Machine Learning, International Machine Learning Society, Lille, 2015, pp. 1180–1189.Google Scholar - [23] Tzeng E., Hoffman J., Saenko K., et al., Adversarialdiscriminative Domain Adaptation[C]∥IEEE Conference on Computer Vision and Pat-Tern Recognition, IEEE Press, Piscataway, 2017, pp. 2962–2971.Google Scholar
- [24] Cao Z.J., Ma L.J., Long M.S., et al.,
Partial adversarial domain adapta-tion[C] , in: European Conference on Computer Vision, Springer In-ternational Publishing, Cham, 2018, pp. 139–155.Google Scholar - [25] Zhang J., Ding Z.W., Li W.Q., et al.,
Importance weighted adversarial nets for partial domain adaptation [C] , in: 2018 IEEE/ CVF Conference on Computer Vision and Pattern Recognition, IEEE Press, Piscataway, 2018, pp. 8156–8164.Google Scholar - [26] Yang J., Liu J., Xu N., Huang J.,
TVT: transferable vision transformer for unsupervised domain adaptation , in: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2023, pp. 520–530, 10.1109/WACV56688.2023.00059.Google ScholarCross Ref - [27] Wang Sheng-sheng, Wang Bilin, Zhang Zhe, Heidari Ali Asghar, Chen Huiling, Class-aware sample reweighting optimal transport for multi-source domain adaptation, Neurocomputing 523 (2022) 213–223.Google ScholarDigital Library
- [28] Gretton A., Borgwardt K.M., Rasch M.J., Schölkopf B., Smola A., A kernel two-sample test, J. Mach. Learn. Res. 13 (Mar. 2012) 723–773.Google ScholarDigital Library
- [29] Long M., Wang J., Ding G., Sun J., Yu P.S.,
Transfer feature learning with joint distribution adaptation , in: Proc. IEEE Int. Conf. Comput. Vis, Dec. 2013, pp. 2200–2207.Google Scholar - [30] Long M., Wang J., Jordan M.I.,
Deep transfer learning with joint adaptation networks , in: Proc. ICML, 2017, pp. 2208–2217.Google Scholar - [31] Yan H., Ding Y., Li P., Wang Q., Xu Y., Zuo W.,
Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation , in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 2272–2281.Google Scholar - [32] Wang J., Feng W., Chen Y., Yu H., Huang M., Yu P.S.,
Visual domain adaptation with manifold embedded distribution alignment , in: Proc. ACM Multimedia Conf. Multimedia Conf. (MM), 2018, pp. 402–410.Google Scholar - [33] Saenko K., Kulis B., Fritz M., Darrell T.,
Adapting visual category models to new domains , in: Proc. ECCV, 2010, pp. 213–226.Google Scholar - [34] Venkateswara H., Eusebio J., Chakraborty S., Panchanathan S., Deep hashing network for unsupervised domain adaptation, 2017, arXiv:1706.07522. [Online]. Available: http://arxiv.org/abs/1706.07522.Google Scholar
- [35] Elhadji-Ille-Gado N., Grall-Maes E., Kharouf M.,
Transfer learning for large scale data using subspace alignment[C] , in: The 16th IEEE International Conference on Machine Learning and Applications, IEEE Press, Pisca-taway, 2017, pp. 1006–1010.Google Scholar - [36] Yu C., Wang J., Chen Y., Huang M.,
Transfer learning with dynamic adversarial adaptation network , in: Proc. IEEE Int. Conf. Data Mining (ICDM), Nov. 2019, pp. 778–786.Google Scholar - [37] Zhu Y., et al., Deep subdomain adaptation network for image classification, IEEE Trans. Neural Netw. Learn. Syst. 32 (4) (Apr. 2020) 1713–1722.Google Scholar
- [38] He X., Chen Y., Ghamisi P., Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network, IEEE Trans. Geosci. Remote Sens. 58 (5) (May 2019) 3246–3263.Google Scholar
- [39] Li L., et al.,
Progressive domain expansion network for single domain generalization , in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2021, pp. 224–233.Google Scholar - [40] Wang Z., Luo Y., Qiu R., Huang Z., Baktashmotlagh M.,
Learning to diversify for single domain generalization , in: Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Oct. 2021, pp. 834–843.Google Scholar - [41] Nam H., Lee H., Park J., Yoon W., Yoo D.,
Reducing domain gap by reducing style bias , in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2021, pp. 8690–8699.Google Scholar - [42] Wang H., Cheng Y., Wang X., A novel hyperspectral image classification method using class-weighted domain adaptation network, Remote Sens. 15 (2023) 999.Google Scholar
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Published in
Image and Vision Computing Volume 141, Issue C
Jan 2024
138 pages
ISSN:0262-8856
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Elsevier B.V.
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Butterworth-Heinemann
United States
Publication History
- Published: 17 April 2024
Author Tags
- Domain adaptation
- Correlation subdomain adaptation
- Maximum mean difference
- Correlation transfer learning
- Deep learning
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