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Return of Frustratingly Easy Domain Adaptation
Open accessAbstract
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being ``frustratingly easy'' to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.
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“The core idea is to mathematically transform the EEG data from different subjects into a shared feature space, thereby reducing the distributional discrepancies caused by inter-subject variability and making the data more comparable for model training (Sun et al., 2016).”
From Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods (2026) · cites this paper as Sun et al., 2016
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“A key strategy within inter-subject TL for EEG-BCIs is data alignment.”
From Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods (2026) · cites this paper as Sun et al., 2016
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
A Singular Value Thresholding Algorithm for Matrix Completion
Cai, J., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,878
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,441
Domain Adaptation via Transfer Component Analysis
Pan, S. J., Tsang, I. W.-H., Kwok, J. T.-Y., & Yang, Q. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,809
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847
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Research works printed but not linked: 1
- Tommasi, T., and Tuytelaars, T. 2014. A testbed for crossdataset analysis. In ECCV TASK-CV Workshop .
Cited by
Sorted by citations
Items 501–600 of 1,005
Musumeci, F., Garbhapu, V. V., Hirota, Y., Awaji, Y., Xu, S., Shiraiwa, M., Mukherjee, B., & Tornatore, M. (2021) · Journal of Optical Communications and Networking · doi:10.1364/jocn.438269 · Cited by 29
Li, J., Horiguchi, Y., & Sawaragi, T. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101516 · Cited by 29
Xu, Q., Wei, X., Bai, R., Li, S., & Meng, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118807 · Cited by 30
Liu, Z., Chen, L., Wei, H., Wu, F.-M., Chen, L., & Chen, Y. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108968 · Cited by 31
Xu, H., Liu, J., Peng, X., Wang, J., & He, C. (2022) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-022-03950-9 · Cited by 29
Double Stage Transfer Learning for Brain–Computer Interfaces
Gao, Y., Li, M., Peng, Y. F., Fang, F. Z., & Zhang, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3241301 · Cited by 30
A Dual-View Style Mixing Network for unsupervised cross-domain fault diagnosis with imbalanced data
Chen, Z., Yu, W., Wang, L., Ding, X., Huang, W., & Shao, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110918 · Cited by 29
Rao, H., Leung, C., & Miao, C. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01864-0 · Cited by 29
Wang, Z., Wan, H., Meng, L., Zeng, Z., Akay, M., Chen, C., & Chen, W. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106086 · Cited by 29
Transfer learning with deep manifold regularized auto-encoders
Zhu, Y. Q., Wu, X., Li, P., Zhang, Y., & Hu, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.078 · Cited by 29
Zeng, Y., Jiang, S., Konstantinou, G. S., Pou, J., Zou, G., & Zhang, X. (2025) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2025.3525500 · Cited by 28
Frustratingly Easy Model Ensemble for Abstractive Summarization
Kobayashi, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1449 · Cited by 28
Functional protein representations from biological networks enable diverse cross-species inference
Fan, J., Cannistra, A. F., Fried, I., Lim, T., Schaffner, T., Crovella, M. E., Hescott, B. J., & Leiserson, M. D. M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz132 · Cited by 26
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification
Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3088012 · Cited by 28
Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation
Ye, S., Wu, K., Zhou, M., Yang, Y., Tan, S. H., Xu, K., Song, J., Bao, C., & Ma, K. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01375 · Cited by 32
Shi, C., Xin, X., & Zhang, J. (2021) · Brain Sciences · doi:10.3390/brainsci11050603 · Cited by 28
Zhang, Z., Shao, M., Ma, C., Lv, Z., & Zhou, J. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-022-07341-6 · Cited by 28
Confidence Regularized Label Propagation Based Domain Adaptation
Wang, W., Li, B., Wang, M., Nie, F., Wang, Z., & Li, H. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3104835 · Cited by 28
A New Progressive Multisource Domain Adaptation Network With Weighted Decision Fusion
Liu, Z., Ning, L., & Zhang, Z. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3179805 · Cited by 28
DomainATM: Domain adaptation toolbox for medical data analysis
Guan, H., & Liu, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119863 · Cited by 31
Parameter Transfer Extreme Learning Machine based on Projective Model
Chen, C., Jiang, B., & Jin, X. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489244 · Cited by 29
Trained 3D Models for CNN based Object Recognition
Sarkar, K., Varanasi, K., & Stricker, D. (2017) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0006272901300137 · Cited by 28
Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration
Tan, S., Jiao, J., & Zheng, W. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00554 · Cited by 26
Homologous Component Analysis for Domain Adaptation
Liu, Y., Tu, W., Du, B., Zhang, L., & Tao, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2929421 · Cited by 27
Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection
Vercruyssen, V., Meert, W., & Davis, J. J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6068 · Cited by 31
Metric Learning in Optimal Transport for Domain Adaptation
Kerdoncuff, T., Emonet, R., & Sebban, M. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/299 · Cited by 29
Domain Invariant and Agnostic Adaptation
Chen, S., Wu, H., & Liu, C. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107192 · Cited by 27
Dynamic transfer partial least squares for domain adaptive regression
Zhao, Z., Yan, G., Ren, M., Cheng, L., Zhu, Z., & Pang, Y. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.08.011 · Cited by 27
Liu, X., Wu, J., Li, W., Liu, Q., Tian, L., & Huang, H. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3233656 · Cited by 29
Yue, R., Jiang, G., Jin, X., He, Q., & Xie, P. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3350147 · Cited by 27
A Multisource–Multitarget Domain Adaptation Method for Rolling Bearing Fault Diagnosis
Chen, Y., & Xiao, L. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3342891 · Cited by 26
Multi-source multi-modal domain adaptation
Zhao, S.-C., Jiang, J., Tang, W.-B., Zhu, J.-K., Chen, H., Xu, P.-F., Schuller, B. W., Tao, J.-H., Yao, H.-X., & Ding, G.-G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102862 · Cited by 28
Joint metric and feature representation learning for unsupervised domain adaptation
Xie, Y., Du, Z., Li, J., Jing, M., Chen, E., & Lü, K. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105222 · Cited by 26
Active vs Transfer Learning Approaches for QoT Estimation with Small Training Datasets
Azzimonti, D., Rottondi, C., Giusti, A., Tornatore, M., & Bianco, A. G. (2020) · Optical Fiber Communication Conference · doi:10.1364/ofc.2020.m4e.1 · Cited by 26
Data Management for Transfer Learning Approaches to Elbow EMG-Torque Modeling
Jiang, X., Bardizbanian, B., Dai, C., Chen, W., & Clancy, E. A. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3069961 · Cited by 26
Margin-Based Adversarial Joint Alignment Domain Adaptation
Zuo, Y., Yao, H., Zhuang, L., & Xu, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3081729 · Cited by 27
Cross-Spectral Iris Recognition by Learning Device-Specific Band
Wei, J., Wang, Y., Li, Y., He, R., & Sun, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3117291 · Cited by 26
Cross-domain object detection using unsupervised image translation
Arruda, V. F., Berriel, R. F., Paixão, T. M., Badue, C. S., Souza, A. F. D., Sebe, N., & Oliveira-Santos, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116334 · Cited by 27
Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition
Murali, P. K., Wang, C., Lee, D., Dahiya, R., & Kaboli, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191408 · Cited by 26
Yuan, Z., Kerckhoffs, J., Hoek, G., & Vermeulen, R. C. H. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c05036 · Cited by 26
Representation learning via an integrated autoencoder for unsupervised domain adaptation
Zhu, Y. Q., Wu, X., Qiang, J., Yuan, Y., & Li, Y. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-022-1349-5 · Cited by 26
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
Yu, C., Wang, J., Chen, Y., & Wu, Z. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851810 · Cited by 27
Unsupervised Domain Adaptation Through Inter-Modal Rotation for RGB-D Object Recognition
Loghmani, M. R., Robbiano, L., Planamente, M., Park, K., Barbara Caputo, B., & Vincze, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3007092 · Cited by 29
Qian, W., Li, S., Wang, J., Xin, Y., & Ma, H. (2018) · 2018 Prognostics and System Health Management Conference (PHM-Chongqing) · doi:10.1109/phm-chongqing.2018.00180 · Cited by 28
Wang, Z. Q., Wang, H., & Saddik, A. E. (2024) · IEEE Access · doi:10.1109/access.2024.3482988 · Cited by 28
Cross-domain knowledge transfer in industrial process monitoring: A survey
Chai, Z., Zhao, C., & Huang, B. (2025) · Journal of Process Control · doi:10.1016/j.jprocont.2025.103408 · Cited by 30
Structural block driven enhanced convolutional neural representation for relation extraction
Wang, D., Tiwari, P., Garg, S., Zhu, H., & Bruza, P. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105913 · Cited by 25
Transferable Feature Selection for Unsupervised Domain Adaptation
Yan, Y., Wu, H., Ye, Y., Bi, C., Lu, M., Liu, D., Wu, Q., & Ng, M. K. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3060037 · Cited by 25
Multi-level Alignment Network for Domain Adaptive Cross-modal Retrieval
Dong, J., Long, Z., Mao, X., Lin, C., He, Y., & Ji, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.114 · Cited by 25
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Wei, P., Ke, Y., Qu, X., & Leong, T.-Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3071244 · Cited by 25
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
Qiang, W., Li, J., Zheng, C., Su, B., & Xiong, H. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3112815 · Cited by 25
Zhang, Y., Yuan, N., Zhang, Z., Du, J., Wang, T., Liu, B., Yang, A., Lv, K., Ma, G., & Lei, B. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102467 · Cited by 25
Qin, A., Mao, H., Sun, K., Huang, Z., & Li, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3164177 · Cited by 25
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Liu, G., Zhao, T., Inae, E., Luo, T., & Jiang, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599497 · Cited by 29
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation
Zhang, Y., Wang, Z., Li, J., Zhuang, J., & Lin, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01046 · Cited by 25
Gao, Z., Han, T. T., Zhu, L., Zhang, H., & Wang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2878313 · Cited by 25
Transformer-Based Source-Free Domain AdaptationPreprint
Yang, G., Tang, H., Zhong, Z., Ding, M., Shao, L., Sebe, N., & Ricci, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14138 · Cited by 25
A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds
Mahadevan, S., Mishra, B., & Ghosh, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_50 · Cited by 24
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Rakshit, S., Banerjee, B., Roig, G., & Chaudhuri, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33676-9_34 · Cited by 24
Collaborative representation with curriculum classifier boosting for unsupervised domain adaptation
Han, C., Zhou, D., Xie, Y., Gong, M., Lei, Y., & Shi, J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107802 · Cited by 24
Qi, Y., Ding, L., Wang, Y., & Pan, G. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3100270 · Cited by 24
Domain adaptation via incremental confidence samples into classification
Teng, S., Zheng, Z., Wu, N., Fei, L., & Zhang, W. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22629 · Cited by 24
Weakly Supervised rPPG Estimation for Respiratory Rate Estimation
Du, J., Liu, S., Zhang, B., & Yuen, P. C. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00271 · Cited by 24
Dual-Level Adaptive and Discriminative Knowledge Transfer for Cross-Domain Recognition
Meng, M., Lan, M., Yu, J., Wu, J., & Liu, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3145235 · Cited by 24
Xu, H., Wang, J., Liu, J., Peng, X., & He, C. (2022) · Measurement · doi:10.1016/j.measurement.2022.111986 · Cited by 24
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Cui, X., Cao, J., Lai, X., Jiang, T., & Gao, F. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3229066 · Cited by 24
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Yang, L., Lu, B., Zhou, Q., & Pan, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110277 · Cited by 24
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Sun, F. J., Wu, H., Luo, Z., Gu, W., Yan, Y., & Du, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2944226 · Cited by 24
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Jin, X., Yang, X., Fu, B., & Chen, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.098 · Cited by 25
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Jing, M., Li, J., Lü, K., Zhu, L., & Yang, Y. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.016 · Cited by 24
Unsupervised neural domain adaptation for document image binarization
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Zhao, P., Wu, T., Zhao, S., & Liu, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107227 · Cited by 24
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Ding, M., Wang, Y., Hemberg, E., & O’Reilly, U.-M. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303794 · Cited by 23
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Sun, Y., Yang, G., Ding, D., Cheng, G., Xu, J., & Li, X. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207358 · Cited by 23
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Zhan, F., & Zhang, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412465 · Cited by 28
Li, W., Li, L., & Yang, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105774 · Cited by 23
Domain adaptation with optimal transport improves EEG sleep stage classifiers
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Cross Domain Mean Approximation for Unsupervised Domain Adaptation
Zang, S., Cheng, Y., Wang, X., Yu, Q., & Xie, G.-S. (2020) · IEEE Access · doi:10.1109/access.2020.3012152 · Cited by 23
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Hu, M., Wu, Y., Zhao, S., Guo, H., Cheng, R., & Su, Z. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1558 · Cited by 23
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Ma, A., You, F., Jing, M., Li, J., & Lü, K. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102367 · Cited by 23
Tang, X., Li, X., Li, W., Hao, B., Xie, Y., & Dang, X. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3082648 · Cited by 21
Shen, M., Zou, B., Li, X., Zheng, Y., Li, L., & Zhang, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103023 · Cited by 24
Zhu, Z., Luo, Y., Chen, S., Qi, G., Mazur, N., Zhong, C., & Li, Q. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103303 · Cited by 22
Spatial filtering based on Riemannian distance to improve the generalization of ErrP classification
Cruz, A., Pires, G., & Nunes, U. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.078 · Cited by 22
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Similarity constraint style transfer mapping for emotion recognition
Chen, L., She, Q., Meng, M., Zhang, Q., & Zhang, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104314 · Cited by 21
A new multi-source Transfer Learning method based on Two-stage Weighted Fusion
Huang, L., Fan, J., Zhao, W., & You, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110233 · Cited by 22
Class Subdomain Adaptation Network for Bearing Fault Diagnosis Under Variable Working Conditions
Zhang, L., Li, H., Cui, J., Li, W., & Wang, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3230479 · Cited by 22
A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects
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Metadata
- Authors
- Sun, B., Feng, J., & Saenko, K.
- Affiliations
- University of Massachusetts Lowell (US)
- University of California, Berkeley (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-03-02
- Volume
- 30
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963275094
- Cited by
- 2,004
- References
- 44
- Citations by year
- 2026: 113 · 2025: 184 · 2024: 246 · 2023: 274 · 2022: 236 · 2021: 315 · 2020: 254 · 2019: 186 · 2018: 125 · 2017: 50 · 2016: 17
- FWCI
- 66.62
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1451244, 1212928)
- Nvidia (Tesla K40)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- unsupervised domain adaptation, domain shift, coral, unlabeled target data
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