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 of data alignment is to mathematically transform EEG data from different subjects into a shared feature space, reducing distributional discrepancies caused by inter-subject variability and making the data more comparable for model training.
“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
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredData alignment is a key strategy within inter-subject transfer learning for EEG-BCIs.
“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
Figure 2 (Related Work, p. 2) describes aligning source and target data as a strategy for domain adaptation. The paper does not address EEG-BCIs or inter-subject transfer learning, so it does not establish the claim in that specific setting. (LLM, conf=0.87)
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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 · see citing works
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 · see citing works
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 · see citing works
A Singular Value Thresholding Algorithm for Matrix Completion
Jian-feng Cai, A., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,778 · see citing works
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,360 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationImageNet: A large-scale hierarchical image database
Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 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/cvprw.2009.5206848 · Cited by 2,714 · see citing works
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285 · see citing works
Self-citation(2007) · CaltechAUTHORS (California Institute of Technology) · Cited by 2,274 · see citing works
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. L. (2012) · 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.2012.6247911 · Cited by 2,254 · see citing works
Analysis of Representations for Domain Adaptation
Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0022 · Cited by 2,019 · see citing works
(2007) · Cited by 2,018 · see citing works
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H.-P., Schölkopf, B., & Smola, A. J. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl242 · Cited by 1,674 · see citing works
Correcting Sample Selection Bias by Unlabeled Data
Huang, J., Smola, A. J., Gretton, A., Borgwardt, K. M., & Schölkopf, B. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0080 · Cited by 1,617 · see citing works
Domain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543 · see citing works
Frustratingly Easy Domain AdaptationPreprint
Daumé, H. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0907.1815 · Cited by 1,471 · see citing works
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.368 · Cited by 1,341 · see citing works
Domain adaptation for object recognition: An unsupervised approach
Gopalan, R., Li, R., & Chellappa, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126344 · Cited by 1,059 · see citing works
Instance Weighting for Domain Adaptation in NLP
Jiang, J., & Zhai, C. (2007) · Cited by 780 · see citing works
Transfer Joint Matching for Unsupervised Domain Adaptation
Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2014) · 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.2014.183 · Cited by 761 · see citing works
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Kulis, B., Saenko, K., & Darrell, T. (2011) · 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.2011.5995702 · Cited by 698 · see citing works
Self-citationFrustratingly Easy Domain Adaptation
Daum, H. (2007) · Cited by 605 · see citing works
Domain Transfer Multiple Kernel Learning
Duan, L., Tsang, I. W.-H., & Xu, D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.114 · Cited by 548 · see citing works
Domain Adaptive Neural Networks for Object Recognition
Ghifary, M., Kleijn, W. B., & Zhang, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13560-1_76 · Cited by 484 · see citing works
Gong, B., Grauman, K. L., & Sha, F. (2013) · Cited by 459 · see citing works
Domain adaptation from multiple sources via auxiliary classifiers
Duan, L., Tsang, I. W.-H., Xu, D., & Chua, T. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553411 · Cited by 299 · see citing works
Discriminative Decorrelation for Clustering and Classification
Hariharan, B., Malik, J., & Ramanan, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_33 · Cited by 271 · see citing works
Generalized Domain-Adaptive Dictionaries
Shekhar, S., Patel, V. M., Nguyen, H. V., & Chellappa, R. (2013) · 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.2013.53 · Cited by 203 · see citing works
Huang, D.-A., & Wang, Y.-C. F. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.310 · Cited by 174 · see citing works
From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains
Sun, B., & Saenko, K. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.82 · Cited by 156 · see citing works
Self-citationAdapting SVM Classifiers to Data with Shifted Distributions
Yang, J. J., Yan, R., & Hauptmann, A. G. (2007) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2007.37 · Cited by 132 · see citing works
DLID: Deep learning for domain adaptation by interpolating between domains
Chopra, S., Balakrishnan, S., & Gopalan, R. (2013) · Cited by 127 · see citing works
Representing Tuple and Attribute Uncertainty in Probabilistic Databases
Sen, P., Deshpande, A., & Getoor, L. (2007) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2007.11 · Cited by 99 · see citing works
Domain Adaptive Neural Networks for Object RecognitionPreprint
Ghifary, M., Kleijn, W. B., & Zhang, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.6041 · Cited by 93 · see citing works
Learning from Multiple OutlooksPreprint
Harel, M., & Mannor, S. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1005.0027 · Cited by 80 · see citing works
Frustratingly Easy NBNN Domain Adaptation
Tommasi, T., & Barbara Caputo, B. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.116 · Cited by 77 · see citing works
A Testbed for Cross-Dataset Analysis
Tommasi, T., & Tuytelaars, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_2 · Cited by 64 · see citing works
Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow
Caseiro, R., Henriques, J. F., Martins, P., & Batista, J. P. (2015) · 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.2015.7299009 · Cited by 51 · see citing works
Flagged first, then by citations
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
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A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678 · see citing works
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154 · see citing works
Self-citationArbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076 · see citing works
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757 · see citing works
Self-citationMaximum Classifier Discrepancy for Unsupervised Domain Adaptation
Saito, K., Watanabe, K., Ushiku, Y., & Harada, T. (2018) · 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.2018.00392 · Cited by 2,278 · see citing works
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209 · see citing works
Deep Hashing Network for Unsupervised Domain Adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.572 · Cited by 1,743 · see citing works
Moment Matching for Multi-Source Domain Adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., & Wang, B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00149 · Cited by 1,731 · see citing works
Self-citationDomain Adaptive Faster R-CNN for Object Detection in the Wild
Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00352 · Cited by 1,672 · see citing works
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603 · see citing works
Towards Personalized Federated Learning
Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,366 · see citing works
Diverse Image-to-Image Translation via Disentangled Representations
Lee, H.-Y., Tseng, H.-Y., Huang, J., Singh, M., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_3 · Cited by 1,285 · see citing works
Unsupervised Domain Adaptation with Residual Transfer NetworksPreprint
Long, M., Han, Z., Wang, J., & Jordan, M. I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04433 · Cited by 1,282 · see citing works
Domain Generalization with Adversarial Feature Learning
Li, H., Pan, S. J., Wang, S., & Kot, A. C. (2018) · 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.2018.00566 · Cited by 1,251 · see citing works
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., & Jiao, J. (2018) · 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.2018.00110 · Cited by 1,054 · see citing works
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Domain adaptation for medical image analysis: A survey
Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057 · see citing works
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Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046 · see citing works
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Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987 · see citing works
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 992 · see citing works
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Shen, J. G., Qu, Y., Zhang, W., & Yu, Y. H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11784 · Cited by 895 · see citing works
Liang, J., Hu, D., & Feng, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08546 · Cited by 858 · see citing works
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Zhang, K., Chao, W., Sha, F., & Grauman, K. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_47 · Cited by 767 · see citing works
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Zhao, W., Queralta, J. P., & Westerlund, T. (2020) · 2020 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308468 · Cited by 941 · see citing works
Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763 · see citing works
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · see citing works
“Finally, we also applied the domain adaptation method Correlation Alignment for Unsupervised Domain Adaptation (CORAL, (Sun et al , 2016)).”
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., & Yu, P. S. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240512 · Cited by 685 · see citing works
Transferable Representation Learning with Deep Adaptation Networks
Long, M., Cao, Y., Cao, Z., Wang, J., & Jordan, M. I. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2868685 · Cited by 680 · see citing works
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Adaptive batch normalization for practical domain adaptation
Li, Y., Wang, N., Shi, J., Hou, X., & Liu, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.005 · Cited by 601 · see citing works
Busto, P. P., & Gall, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.88 · Cited by 568 · see citing works
Fu, Y. L., Wei, Y., Wang, G., Zhou, Y., Shi, H., Uiuc, U., & Huang, T. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00621 · Cited by 568 · see citing works
VisDA: The Visual Domain Adaptation ChallengePreprint
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Xu, R., Li, G., Yang, J., & Lin, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00151 · Cited by 538 · see citing works
Zhao, Z., Zhang, Q., Yu, X., Sun, C., Wang, S., Yan, R., & Chen, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3116309 · Cited by 580 · see citing works
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He, H., & Wu, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913914 · Cited by 525 · see citing works
“This idea has been widely used in TL [24], [31], [40].”
BackgroundMethodInfluentialDemystifying Neural Style Transfer
Li, Y., Wang, N., Liu, J., & Hou, X. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/310 · Cited by 539 · see citing works
Generalizing a Person Retrieval Model Hetero- and Homogeneously
Zhong, Z., Zheng, L., Li, S., & Yang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_11 · Cited by 506 · see citing works
Estimating individual treatment effect: generalization bounds and algorithmsPreprint
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Zhang, Y., David, P., & Gong, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.223 · Cited by 504 · see citing works
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Ghifary, M., Balduzzi, D., Kleijn, W. B., & Zhang, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599532 · Cited by 466 · see citing works
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Rozantsev, A., Salzmann, M., & Fua, P. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2814042 · Cited by 466 · see citing works
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Yu, C., Wang, J., Chen, Y., & Huang, M. (2019) · Proceedings · doi:10.1109/icdm.2019.00088 · Cited by 454 · see citing works
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Chang, W.-G., You, T., Seo, S., Kwak, S., & Han, B. (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.2019.00753 · Cited by 446 · see citing works
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James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., & Bousmalis, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01291 · Cited by 420 · see citing works
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
Pan, Y., Yao, T., Li, Y., Wang, Y., Ngo, C., & Mei, T. (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.2019.00234 · Cited by 413 · see citing works
Revisiting Batch Normalization For Practical Domain AdaptationPreprint
Li, Y., Wang, N., Shi, J., Liu, J., & Hou, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.04779 · Cited by 399 · see citing works
Remaining useful lifetime prediction via deep domain adaptation
da Costa, P. R. D. O., Akçay, A., Zhang, Y., & Kaymak, U. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106682 · Cited by 390 · see citing works
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Hoyer, L., Dai, D., Wang, H., & Gool, L. V. (2023) · 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/cvpr52729.2023.01128 · Cited by 381 · see citing works
Fully Convolutional Adaptation Networks for Semantic Segmentation
Zhang, Y., Qiu, Z., Yao, T., Liu, D., & Mei, T. (2018) · 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.2018.00712 · Cited by 373 · see citing works
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 367 · see citing works
Current progress and open challenges for applying deep learning across the biosciences
Sapoval, N., Aghazadeh, A., Nute, M., Antunes, D. A., Balaji, A., Baraniuk, R. G., Barberan, C., Dannenfelser, R., Dun, C., Edrisi, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29268-7 · Cited by 361 · see citing works
A Broader Study of Cross-Domain Few-Shot Learning
Guo, Y., Codella, N., Karlinsky, L., Codella, J., Smith, J. R., Saenko, K., Rosing, T., & Feris, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_8 · Cited by 358 · see citing works
Self-citationAD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification
Zhai, Y., Lu, S., Ye, Q., Shan, X., Chen, J., Ji, R., & Tian, Y. (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/cvpr42600.2020.00904 · Cited by 341 · see citing works
A Survey of Transfer Learning for Convolutional Neural Networks
Ribani, R., & Marengoni, M. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi-t.2019.00010 · Cited by 330 · see citing works
Domain Adaptation for Visual Applications: A Comprehensive SurveyPreprint
Csurka, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05374 · Cited by 324 · see citing works
AutoDIAL: Automatic Domain Alignment Layers
Carlucci, F. M., Porzi, L., Barbara Caputo, B., Ricci, E., & Bulò, S. R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.542 · Cited by 318 · see citing works
Adaptive Transfer Network for Cross-Domain Person Re-Identification
Liu, J., Zha, Z.-J., Chen, D., Hong, R., & Wang, M. (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.2019.00737 · Cited by 323 · see citing works
A recent overview of the state-of-the-art elements of text classification
Mirończuk, M., & Protasiewicz, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.058 · Cited by 317 · see citing works
Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314 · see citing works
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
Chen, Y., Li, W., & Gool, L. V. (2018) · 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.2018.00823 · Cited by 303 · see citing works
Unsupervised Domain Adaptation with Similarity Learning
Pinheiro, P. O. (2018) · 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.2018.00835 · Cited by 300 · see citing works
Learning Linear Transformations for Fast Image and Video Style Transfer
Li, X., Liu, S., Kautz, J., & Yang, M. (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.2019.00393 · Cited by 295 · see citing works
Cheng, C., Zhou, B., Ma, G., Wu, D., & Yuan, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.040 · Cited by 293 · see citing works
Unsupervised Domain Adaptation of Object Detectors: A Survey
Oza, P., Sindagi, V. A., VS, V., & Patel, V. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217046 · Cited by 313 · see citing works
Multi-representation adaptation network for cross-domain image classification
Zhu, Y., Zhuang, F., Wang, J., Chen, J., Shi, Z., Wu, W., & He, Q. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.07.010 · Cited by 287 · see citing works
Zhang, K., Robinson, N., Lee, S., & Guan, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.12.013 · Cited by 286 · see citing works
Generalized Source-free Domain Adaptation
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., & Jui, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00885 · Cited by 286 · see citing works
A Robust Learning Approach to Domain Adaptive Object Detection
Khodabandeh, M., Vahdat, A., Ranjbar, M., & Macready, W. G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00057 · Cited by 276 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“For example, methods that learn features that have similar distributions across domains are equally applicable to both settings (Ben-David et al., 2006; Long et al., 2015; Sun et al., 2016; Ganin et al., 2016; Tzeng et al., 2017; Shen et al., 2018; Wu et al., 2019).”
A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions
Zhu, J., Chen, N., & Shen, C. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2936932 · Cited by 267 · see citing works
Dong, Y., Li, Y., Zheng, H., Wang, R., & Xu, M. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.03.042 · Cited by 268 · see citing works
Haeusser, P., Frerix, T., Mordvintsev, A., & Cremers, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.301 · Cited by 247 · see citing works
DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation
Wu, Z., Han, X., Lin, Y.-L., Uzunbaş, M. G., Goldstein, T., Lim, S. N., & Davis, L. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_32 · Cited by 256 · see citing works
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Liang, J., Hu, D., Wang, Y., He, R., & Feng, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3103390 · Cited by 248 · see citing works
Self-citationUnsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Wang, Q., & Breckon, T. P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6091 · Cited by 241 · see citing works
Domain Adaptation: Challenges, Methods, Datasets, and Applications
Singhal, P., Walambe, R. A., Ramanna, S., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3237025 · Cited by 253 · see citing works
From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN
Russo, P., Carlucci, F. M., Tommasi, T., & Barbara Caputo, B. (2018) · 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.2018.00845 · Cited by 244 · see citing works
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
Chen, C., Chen, Z., Jiang, B., & Jin, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013296 · Cited by 242 · see citing works
Delving into deep imbalanced regression
Yang, Y., Zha, K., Chen, Y.-C., Wang, H., & Katabi, D. (2021) · arXiv · doi:10.48550/arxiv.2102.09554 · Cited by 229 · see citing works
HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation
Chen, C., Fu, Z., Chen, Z., Jin, S., Cheng, Z., Jin, X., & Hua, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5745 · Cited by 226 · see citing works
Multi-Source Distilling Domain Adaptation
Zhao, S., Wang, G., Zhang, S., Gu, Y., Li, Y., Song, Z., Xu, P., Hu, R., Chai, H., & Keutzer, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6997 · Cited by 229 · see citing works
Residual joint adaptation adversarial network for intelligent transfer fault diagnosis
Jiao, J., Zhao, M. J., Lin, J., & Liang, K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106962 · Cited by 227 · see citing works
Universal Domain Adaptation through Self SupervisionPreprint
Saito, K., Kim, D., Sclaroff, S., & Saenko, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07953 · Cited by 235 · see citing works
Self-citationCross-Domain Contrastive Learning for Unsupervised Domain Adaptation
Wang, R., Wu, Z., Weng, Z., Chen, J., Qi, G.-J., & Jiang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3146744 · Cited by 225 · see citing works
Domain Adaptation in Remote Sensing Image Classification: A Survey
Peng, J., Huang, Y., Sun, W., Chen, N., Ning, Y., & Du, Q. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3220875 · Cited by 221 · see citing works
Qian, Q., Wang, Y., Zhang, T., & Qin, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110748 · Cited by 223 · see citing works
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
Deitke, M., Han, W., Herrasti, A., Kembhavi, A., Kolve, E., Mottaghi, R., Salvador, J., Schwenk, D., VanderBilt, E., Wallingford, M., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00323 · Cited by 220 · see citing works
Machine learning and algorithmic fairness in public and population health
Mhasawade, V., Zhao, Y., & Chunara, R. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00373-4 · Cited by 217 · see citing works
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
Zhao, F., Liao, S., Xie, G.-S., Zhao, J., Zhang, K., & Shao, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_31 · Cited by 217 · see citing works
Mali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Müller, H., Primakov, S. P., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090842 · Cited by 218 · see citing works
Qian, Q., Qin, Y., Wang, Y., & Liu, F. (2021) · Measurement · doi:10.1016/j.measurement.2021.109352 · Cited by 207 · see citing works
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Gholami, B., Sahu, P., Rudovic, O. O., Bousmalis, K., & Pavlović, V. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2963389 · Cited by 197 · see citing works
- 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 · 2015: 2 · 2014: 1 · 2013: 1
- 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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