The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citation
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
Sorted by citations
Items 101–200 of 1,005
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 204
VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation
Peng, X., Usman, B., Kaushik, N., Wang, D., Hoffman, J., & Saenko, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00271 · Cited by 196
Self-citationUniverSeg: Universal Medical Image Segmentation
Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01960 · Cited by 195
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Zhao, S., Fu, H., Gong, M., & Tao, D. (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.2019.01002 · Cited by 190
A hierarchical deep domain adaptation approach for fault diagnosis of power plant thermal system
Wang, X., He, H., & Li, L. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2899118 · Cited by 185
Easy Transfer Learning By Exploiting Intra-Domain Structures
Wang, J., Chen, Y., Yu, H., Huang, M., & Yang, Q. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00211 · Cited by 186
Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces
Zhang, W., & Wu, D. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2985996 · Cited by 180
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss
Roy, S., Siarohin, A., Sangineto, E., Bulò, S. R., Sebe, N., & Ricci, E. (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.2019.00970 · Cited by 171
Li, Q., Shen, C., Chen, L., & Zhu, Z. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107095 · Cited by 174
Two-Branch Attention Adversarial Domain Adaptation Network for Hyperspectral Image Classification
Huang, Y., Peng, J., Sun, W., Chen, N., Du, Q., Ning, Y., & Su, H. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3215677 · Cited by 180
CDTrans: Cross-domain Transformer for Unsupervised Domain AdaptationPreprint
Xu, T., Chen, W., Wang, P., Wang, F., Li, H., & Jin, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06165 · Cited by 220
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-identification
Li, J., & Zhang, S. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_29 · Cited by 174
Qian, W., Li, S., Yi, P., & Zhang, K. (2019) · Measurement · doi:10.1016/j.measurement.2019.02.073 · Cited by 171
A Fine-Grained Adversarial Network Method for Cross-Domain Industrial Fault Diagnosis
Chai, Z., & Zhao, C. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2957232 · Cited by 166
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Bucci, S., Loghmani, M. R., & Tommasi, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_25 · Cited by 165
Ethical issues in using ambient intelligence in health-care settings
Martinez‐Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S., Wieten, S. E., Cho, M. K., Magnus, D. C., Fei-Fei, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30275-2 · Cited by 184
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
Ke, Z., Sun, J., Li, K., Yan, Q., & Lau, R. W. H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19999 · Cited by 202
Arbitrary Style Transfer in Real-time with Adaptive Instance NormalizationPreprint
Huang, X., & Belongie, S. (2017) · arXiv (Cornell University) · Cited by 205
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain AdaptationPreprint
Yang, S., wang, Y., van de Weijer, J., Herranz, L., & Jui, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04202 · Cited by 160
Deep Residual Correction Network for Partial Domain Adaptation
Li, S., Liu, C. H., Lin, Q., Wen, Q., Su, L., Huang, G. M., & Ding, Z. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2964173 · Cited by 162
Deep Adversarial Subdomain Adaptation Network for Intelligent Fault Diagnosis
Liu, Y., Wang, Y., Chow, T. W. S., & Li, B. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3141783 · Cited by 160
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
Ahmed, S. M., Raychaudhuri, D. S., Paul, S., Oymak, S., & Roy–Chowdhury, A. K. (2021) · 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/cvpr46437.2021.00997 · Cited by 159
Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., Carrotta, A., et al. (2020) · Information · doi:10.3390/info11040202 · Cited by 158
Heterogeneous Domain Adaptation: An Unsupervised Approach
Liu, F., Zhang, G., & Jie Lu, B. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2973293 · Cited by 151
Transferring policy of deep reinforcement learning from simulation to reality for robotics
Ju, H., Juan, R., Gomez, R., Nakamura, K., & Li, G. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00573-6 · Cited by 147
A Deeper Look at Facial Expression Dataset Bias
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2973158 · Cited by 149
Guan, H., Liu, Y., Yang, E., Yap, P., Shen, D. G., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102076 · Cited by 153
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
Zhang, Y., David, P., Foroosh, H., & Gong, B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903401 · Cited by 146
Huang, Y., Peng, J., Chen, N., Sun, W., Du, Q., Ren, K., & Huang, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.009 · Cited by 142
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization
Mirza, M. J., Micorek, J., Possegger, H., & Bischof, H. (2022) · 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/cvpr52688.2022.01435 · Cited by 151
Ghorvei, M., Kavianpour, M., Beheshti, M. T. H., & Ramezani, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.057 · Cited by 140
Qian, Q., Zhang, B., Li, C., Mao, Y., & Qin, Y. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111837 · Cited by 150
Transfer learning for detecting unknown network attacks
Zhao, J., Shetty, S. S., Pan, J. W., Kamhoua, C., & Kwiat, K. (2019) · EURASIP Journal on Information Security · doi:10.1186/s13635-019-0084-4 · Cited by 139
Ren, H., Liu, W., Shan, M., & Wang, X. (2019) · Measurement · doi:10.1016/j.measurement.2019.106906 · Cited by 139
Remote Sensing Image Registration Using Convolutional Neural Network Features
Ye, F., Su, Y., Xiao, H., Zhao, X., & Min, W. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2781741 · Cited by 142
Su, S., Li, W., Mou, J., Garg, A., Gao, L., & Liu, J. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2022.3204843 · Cited by 136
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation
Liang, J., He, R., Sun, Z., & Tan, T. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2832198 · Cited by 135
FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis
Yang, H., He, H. X., Zhang, W., & Cao, X. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.2996612 · Cited by 134
Unsupervised Domain Adaptation via Regularized Conditional Alignment
Cicek, S., & Soatto, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00150 · Cited by 135
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Ye, J., Fu, C., Zheng, G., Paudel, D. P., & Chen, G. (2022) · 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/cvpr52688.2022.00869 · Cited by 140
Zhao, G., Li, G., Xu, R., & Lin, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_6 · Cited by 126
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation
Wei, G., Lan, C., Zeng, W., & Chen, Z. (2021) · 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/cvpr46437.2021.01637 · Cited by 133
Domain Generalization with Domain-Specific Aggregation Modules
D’Innocente, A., & Barbara Caputo, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12939-2_14 · Cited by 128
Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain
Ren, C.-X., Liu, Y., Zhang, X.-W., & Huang, K.-K. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3152052 · Cited by 129
Venkateswara, H., Chakraborty, S., & Panchanathan, S. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2740460 · Cited by 127
Zhou, R., Zhang, Z., Fu, H. C., Zhang, L., Li, L., Huang, G., Li, F., Yang, X., Dong, Y., Zhang, Y., et al. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3288118 · Cited by 138
Don’t push the button! Exploring data leakage risks in machine learning and transfer learning
Apicella, A., Isgrò, F., & Prevete, R. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11326-3 · Cited by 176
Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery
Jin, T., Yan, C., Chen, C., Yang, Z., Tian, H., & Wang, S. (2021) · Measurement · doi:10.1016/j.measurement.2021.109639 · Cited by 123
Kheddar, H., Himeur, Y., & Awad, A. I. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103760 · Cited by 141
A deep transfer learning approach for wearable sleep stage classification with photoplethysmography
Radha, M., Fonseca, P. M., Moreau, A., Ross, M., Cerny, A., Anderer, P., Long, X., & Aarts, R. M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00510-8 · Cited by 136
Exploring uncertainty in pseudo-label guided unsupervised domain adaptation
Liang, J., He, R., Sun, Z., & Tan, T. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.106996 · Cited by 119
Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification
Li, M., Li, C.-G., & Guo, J. H. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3173163 · Cited by 120
Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors
Koniusz, P., Tas, Y., & Porikli, F. (2017) · 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.2017.755 · Cited by 120
Deep Unsupervised Convolutional Domain Adaptation
Zhuo, J., Wang, S., Zhang, W., & Huang, Q. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123292 · Cited by 123
Kuang, J., Xu, G., Tao, T., & Wu, Q. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3136175 · Cited by 122
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Guo, H., Pasunuru, R., & Bansal, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6288 · Cited by 119
Bispace Domain Adaptation Network for Remotely Sensed Semantic Segmentation
Liu, W., Su, F., Jin, X., Li, H., & Qin, R. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3035561 · Cited by 115
A Novel Transfer Learning Approach in Remaining Useful Life Prediction for Incomplete Dataset
Siahpour, S., Li, X., & Lee, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3162283 · Cited by 115
Qian, Q., Pu, H., Tu, T., & Qin, Y. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111544 · Cited by 116
A Survey on Deep Transfer Learning and Beyond
Yu, F., Xiu, X., & Li, Y. (2022) · Mathematics · doi:10.3390/math10193619 · Cited by 119
Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis
Jiao, J., Zhao, M. J., Lin, J., & Ding, C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2956294 · Cited by 112
Hu, T., Guo, Y.-M., Gu, L., Zhou, Y.-F., Zhang, Z.-S., & Zhou, Z.-T. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108265 · Cited by 104
Learning an Invariant Hilbert Space for Domain Adaptation
Herath, S., Harandi, M. T., & Porikli, F. (2017) · 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.2017.421 · Cited by 113
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Sohn, K., Liu, S., Zhong, G., Yu, X., Yang, M., & Chandraker, M. K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.630 · Cited by 110
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Fang, K., Bai, Y., Hinterstoißer, S., Savarese, S., & Kalakrishnan, M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461041 · Cited by 112
Class-Specific Reconstruction Transfer Learning for Visual Recognition Across Domains
Wang, S., Zhang, L., Zuo, W., & Zhang, B. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2948480 · Cited by 111
Unsupervised Domain Adaptation With Distribution Matching Machines
Cao, Y., Long, M., & Wang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11792 · Cited by 114
Synthetic to Real Adaptation with Generative Correlation Alignment Networks
Peng, X., & Saenko, K. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00219 · Cited by 116
Self-citationLEEP: A New Measure to Evaluate Transferability of Learned RepresentationsPreprint
Nguyen, C. V., Hassner, T., Seeger, M., & Archambeau, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12462 · Cited by 122
Unsupervised Domain Adaptation With Adversarial Residual Transform Networks
Cai, G., Wang, Y., He, L., & Zhou, M. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2935384 · Cited by 112
Chai, Z., Zhao, C., & Huang, B. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3067786 · Cited by 112
Attending to Discriminative Certainty for Domain Adaptation
Kurmi, V. K., Kumar, S., & Namboodiri, V. P. (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.2019.00058 · Cited by 107
VDM-DA: Virtual Domain Modeling for Source Data-Free Domain Adaptation
Tian, J., Zhang, J., Li, W., & Xu, D. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3111034 · Cited by 110
Generalizing Eye Tracking With Bayesian Adversarial Learning
Wang, K., Zhao, R., Su, H., & Ji, Q. (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.2019.01218 · Cited by 110
Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis
Xia, Y., Shen, C., Wang, D., Shen, Y., Huang, W., & Zhu, Z. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108697 · Cited by 109
Liu, Z., Huang, L., Zhou, K., & Denœux, T. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2995862 · Cited by 107
One-Class Classification: A SurveyPreprint
Perera, P., Oza, P., & Patel, V. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03064 · Cited by 168
An Embarrassingly Simple Approach to Visual Domain Adaptation
Lü, H., Shen, C., Cao, Z., Xiao, Y., & van den Hengel, A. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2819503 · Cited by 103
Qin, R., & Liu, T. (2022) · Remote Sensing · doi:10.3390/rs14030646 · Cited by 121
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Sun, Y., Gao, K., Wu, Z., Li, G., Zong, X., Lei, Z., Wei, Y. J., Ma, J., Yang, X., Feng, X., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3055428 · Cited by 106
A fault diagnosis method of bearings based on deep transfer learning
Huang, M., Yin, J., Yan, S., & Xue, P. (2022) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2022.102659 · Cited by 105
Faster Domain Adaptation Networks
Li, J., Jing, M., Su, H., Lü, K., Zhu, L., & Shen, H. T. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3060473 · Cited by 104
Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance
Chen, P., Zhao, R., He, T., Wei, K., & Yang, Q. (2022) · ISA Transactions · doi:10.1016/j.isatra.2021.12.037 · Cited by 103
Robust unsupervised domain adaptation for neural networks via moment alignment
Zellinger, W., Moser, B., Grubinger, T., Lughofer, E. D., Natschläger, T., & Saminger‐Platz, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.025 · Cited by 101
Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification
Peng, J., Sun, W., Ma, L., & Du, Q. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2889789 · Cited by 99
Pixel and feature level based domain adaptation for object detection in autonomous driving
Shan, Y., Lu, W. F., & Chew, C. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.022 · Cited by 101
Zhao, S., Wang, Y., Li, B., Wu, B., Gao, Y., Xu, P., Darrell, T., & Keutzer, K. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16464 · Cited by 99
Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis
Shao, X., & Kim, C. S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121216 · Cited by 102
Style Normalization and Restitution for Domain Generalization and Adaptation
Jin, X., Lan, C., Zeng, W., & Chen, Z. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3104379 · Cited by 114
DINE: Domain Adaptation from Single and Multiple Black-box Predictors
Liang, J., Hu, D., Feng, J., & He, R. (2022) · 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/cvpr52688.2022.00784 · Cited by 100
Self-citationHu, Q., Si, X., Qin, A., Lv, Y., & Liu, M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3174396 · Cited by 97
Rethinking Triplet Loss for Domain Adaptation
Deng, W., Zheng, L., Sun, Y., & Jiao, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2968484 · Cited by 96
Domain Adaptation in the Absence of Source Domain Data
Chidlovskii, B., Clinchant, S., & Csurka, G. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939716 · Cited by 95
Part-aware Progressive Unsupervised Domain Adaptation for Person Re-Identification
Yang, F., Yan, K., Lu, S., Jia, H., Xie, D., Yu, Z., Guo, X., Huang, F., & Gao, W. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3001522 · Cited by 94
Dong, Y., Liang, T., Zhang, Y., & Du, B. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3004263 · Cited by 96
Unsupervised Domain Adaptation With Variational Approximation for Cardiac Segmentation
Wu, F., & Zhuang, X. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3090412 · Cited by 96
Open Set Domain Adaptation for Image and Action Recognition
Busto, P. P., Iqbal, A., & Gall, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2880750 · Cited by 97
Transfer learning in agriculture: a review
Hossen, M. I., Awrangjeb, M., Pan, S., & Mamun, A. A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11081-x · Cited by 126
Qian, W., Li, S., & Wang, J. (2018) · IEEE Access · doi:10.1109/access.2018.2880770 · Cited by 91
Shashikumar, S. P., Shah, A. J., Clifford, G. D., & Nemati, S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219912 · Cited by 93
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
