Return of Frustratingly Easy Domain Adaptation

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

Domain adaptation-based deep feature learning method with a mixture of distance measures for bearing fault diagnosis

Zhou, K., Cao, G., Zhang, K., & Liu, J. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/abeddd · Cited by 17

Convolutional Neural Network‐Based Adaptive Localization for an Ensemble Kalman Filter

Wang, Z., Lei, L., Anderson, J. G., Tan, Z., & Zhang, Y. (2023) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms003642 · Cited by 16

Adversarial decoupling domain generalization network for cross-scene hyperspectral image classification

Zhao, H., Lin, L., Wang, J., Gao, S., & Zhang, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113432 · Cited by 17

Training data selection for imbalanced cross-project defect prediction

Zheng, S., Gai, J., Yu, H., Zou, H., & Gao, S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107370 · Cited by 16

MSLM-RF: A Spatial Feature Enhanced Random Forest for On-Board Hyperspectral Image Classification

Yuan, S., Sun, Y., He, W., Gu, Q., Xu, S., Mao, Z., & Tu, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194075 · Cited by 16

Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

Rostami, M., & Galstyan, A. (2020) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26151 · Cited by 32

Transfer multiscale adaptive convolutional neural network for few-shot and cross-domain bearing fault diagnosis

Li, F., Wang, L., Wang, D., Jun, W., & Zhao, H. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/aced5b · Cited by 17

Hierarchical Style-Aware Domain Generalization for Remote Physiological Measurement

Wang, J., Lü, H., Wang, A., Chen, Y.-C., & He, D. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3346057 · Cited by 19

MDLR: A Multi-Task Disentangled Learning Representations for unsupervised time series domain adaptation

Liu, Y., Li, D., Wang, J., Li, B., & Hang, B. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2023.103638 · Cited by 18

Open-Set Graph Domain Adaptation via Separate Domain Alignment

Wang, Y., Zhu, R., Ji, P., & Li, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28765 · Cited by 16

DCST: Dual Cross-Supervision for Transformer-based Unsupervised Domain Adaptation

Cheng, Y., Yao, P., Xu, L., Chen, M., Liu, P., Shao, P., Shen, S., & Xu, R. X. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106749 · Cited by 17

Cross-database mammographic image analysis through unsupervised domain adaptation

Kumar, D., Kumar, C., & Shao, M. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258419 · Cited by 16

DACH: Domain Adaptation Without Domain Information

Cai, R., Li, J., Zhang, Z., Yang, X., & Hao, Z. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2962817 · Cited by 17

Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

Cartas, A., Radeva, P. I., & Dimiccoli, M. (2020) · IEEE Access · doi:10.1109/access.2020.2990333 · Cited by 18

Label Disentangled Analysis for unsupervised visual domain adaptation

Xiao, N., Zhang, L., Xu, X., Guo, T., & Ma, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107309 · Cited by 14

Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Li, T., Chen, X., Dong, Z., Keutzer, K., & Zhang, S. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/585 · Cited by 17

Online Cross-subject Emotion Recognition from ECG via Unsupervised Domain Adaptation

He, W., Ye, Y., Li, Y., Pan, T., & Lu, L. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630433 · Cited by 16

Cross-domain collaborative learning for single image deraining

Pan, Z., Wang, J., Shen, Z., Han, S., & Zhu, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118611 · Cited by 16

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain AdaptationPreprint

Li, Z., Fang, Z., Liu, F., Yuan, B., Zhang, G., & Jie Lu, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13022 · Cited by 16

Domain-Adaptive Graph Attention Semi-Supervised Network for Temperature-Resilient SHM of Composite Plates

Rezazadeh, N., Luca, A. D., Perfetto, D., Lamanna, G., Annaz, F. Y., & Oliveira, M. D. (2025) · Sensors · doi:10.3390/s25226847 · Cited by 15

Label propagation with multi-stage inference for visual domain adaptation

Han, C., Zhou, D., Xie, Y., Lei, Y., & Shi, J. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106809 · Cited by 15

Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

Li, Z., Cai, R., Ng, H. W., Winslett, M., Fu, T. Z. J., Xu, B., Yang, X., & Zhang, Z. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3445033 · Cited by 15

Rapid Bayesian Inference for Expensive Stochastic Models

Warne, D. J., Baker, R. E., & Simpson, M. J. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.2000419 · Cited by 16

Manifold discrimination partial adversarial domain adaptation

He, C., Zheng, L., Tan, T., Fan, X., & Ye, Z. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109320 · Cited by 15

Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification

Lee, K. W., Jawade, B., Mohan, D. D., Setlur, S., & Govindaraju, V. (2022) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss56176.2022.9959509 · Cited by 15

Personalized visual encoding model construction with small data

Gu, Z., Jamison, K. W., Sabuncu, M. R., & Kuceyeski, A. F. (2022) · Communications Biology · doi:10.1038/s42003-022-04347-z · Cited by 16

Reference Based Sketch Extraction via Attention Mechanism

Ashtari, A., Seo, C. W., Kang, C., Cha, S., & Noh, J. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555504 · Cited by 15

Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression

Wei, P., Ke, Y., Ong, Y.-S., & Ma, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3219121 · Cited by 15

Cross-Subject Seizure Detection by Joint-Probability-Discrepancy-Based Domain Adaptation

Cui, X., Wang, T., Lai, X., Jiang, T., Gao, F., & Cao, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3248101 · Cited by 15

Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

Zheng, Y., Deng, H., Wu, J., Wang, R.-S., Liu, Z.-K., Wu, L., Mao, X.-C., & Chen, J. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2241432 · Cited by 15

Explainable cross-task adaptive transfer learning for motor imagery EEG classification

Miao, M., Yang, Z., Zeng, H., Zhang, W., Bao-guo, X., & Hu, W. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad0c61 · Cited by 15

sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Guo, Y., Liu, J., Wu, Y., Jiang, X., Wang, Y., Meng, L., Liu, X., Shu, F., Dai, C., & Chen, W. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065724500102 · Cited by 15

Multi-source refined adversarial domain adaptation with transfer complementarity infusion for IoT intrusion detection under limited samples

Li, K., Ma, W., Huawei, D., & Xie, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124352 · Cited by 15

Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance

Wang, T., Ke, Y., Huang, Y., He, F., Zhong, W., Liu, S., & Ming, D. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3452410 · Cited by 15

Domain Adaptive Fisher Vector for Visual Recognition

Niu, L., Cai, J., & Xu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_33 · Cited by 15

Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set

Ionescu, R. T., & Butnaru, A. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1135 · Cited by 15

Hybrid Cross Deep Network for Domain Adaptation and Energy Saving in Visual Internet of Things

Zhang, Z., & Li, D. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2867083 · Cited by 15

Joint Domain Matching and Classification for cross-domain adaptation via ELM

Chen, C., Jiang, B., Cheng, Z., & Jin, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.056 · Cited by 15

Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes

Liu, Y., Li, M., Shen, C., Hu, Q., Wen, J., & Xu, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2987632 · Cited by 15

A novel unsupervised adversarial domain adaptation network for remotely sensed scene classification

Liu, W., & Su, F. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1736727 · Cited by 14

Transfer Learning across Different Lightpaths for Failure-Cause Identification in Optical Networks

Musumeci, F., Garbhapu, V. V., Hirota, Y., Awaji, Y., Xu, S., Shiraiwa, M., Mukherjee, B., & Tornatore, M. (2020) · European Conference on Optical Communication · doi:10.1109/ecoc48923.2020.9333305 · Cited by 15

Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold

Jiang, Q., Zhang, Y., & Zheng, K. W. (2022) · Brain Sciences · doi:10.3390/brainsci12050659 · Cited by 15

Applying Federated Learning in the detection of Freezing of Gait in Parkinson’s disease

Jorge, J., Barros, P. H. C. D. A., Yokoyama, R. T., Guidoni, D. L., Ramos, H. S., da Fonseca, N. L. S., & Villas, L. A. (2022) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00037 · Cited by 15

Anatomy-guided domain adaptation for 3D in-bed human pose estimation

Bigalke, A., Hansen, L., Diesel, J., Hennigs, C., Rostalski, P., & Heinrich, M. (2022) · Medical Image Anal. · doi:10.1016/j.media.2023.102887 · Cited by 23

VAE-based Domain Adaptation for Speaker Verification

Wang, X., Li, L., & Wang, D. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023015 · Cited by 14

Correlation Alignment by Riemannian Metric for Domain AdaptationPreprint

Morerio, P., & Murino, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08180 · Cited by 20

Detecting dementia from written and spoken language

Masrani, V. (2018) · cIRcle (University of British Columbia) · doi:10.14288/1.0362923 · Cited by 18

Domain-Invariant Adversarial Learning for Unsupervised Domain AdaptionPreprint

Zhang, Y., Zhang, Y., Wang, Y., & Tian, Q. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12751 · Cited by 16

Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions

Rieman, D., Jaidka, K., Schwartz, H. A., & Ungar, L. (2017) · International Joint Conference on Natural Language Processing · Cited by 14

Tackling unsupervised multi-source domain adaptation with optimism and consistency

Pernes, D., & Cardoso, J. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116486 · Cited by 15

A dual-view alignment-based domain adaptation network for fault diagnosis

Zhao, C., Liu, G., & Shen, W. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac100e · Cited by 14

Domain Robust Deep Embedding Learning for Speaker Recognition

Hu, H.-R., Song, Y., Liu, Y., Dai, L.-R., McLoughlin, I. V., & Liu, L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747364 · Cited by 15

Investigation of deep transfer learning for cross-turbine diagnosis of wind turbine faults

Xie, P., Zhang, X., Jiang, G., Cui, J., & He, Q. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/acadf7 · Cited by 14

Deep transfer learning methods for typical supervised tasks in industrial monitoring: state-of-the-art, challenges, and perspectives

柴, 铮., 汪, 嘉., Zhao, C., 丁, 进., & 孙, 优. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0328 · Cited by 14

Cross-Domain Adaptative Learning for Online Advertisement Customer Lifetime Value Prediction

Su, H., Du, Z., Li, J., Zhu, L., & Lü, K. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25583 · Cited by 17

Implementing and understanding the unsupervised transfer learning in metal organic framework toward methane adsorption from hypothetical to experimental data

Wei, X., Lu, Z., Ai, Y., Shen, L. F., Wei, M., & Wang, X. (2023) · Separation and Purification Technology · doi:10.1016/j.seppur.2023.125291 · Cited by 15

EarSE

Duan, D., Chen, Y., Xu, W., & Li, T. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631447 · Cited by 14

The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Zara, G., Conti, A., Roy, S., Lathuilière, S., Rota, P., & Ricci, E. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00946 · Cited by 16

Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images

Guo, X., Yin, J., & Yang, J. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2319939 · Cited by 14

From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios

Liu, G., Jiao, Y., Chen, J.-J., Zhu, B., & Jiang, Y.-G. (2024) · IEEE transactions on multimedia · doi:10.1109/tmm.2024.3371212 · Cited by 19

Rolling Bearing Fault Diagnosis Using Deep Transfer Learning Based on Joint Generalized Sliced Wasserstein Distance

Lei, N., Cui, J., Han, J., Xian, C., & Tang, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3375400 · Cited by 16

Selecting reliable instances based on evidence theory for transfer learning

Lv, Y., Zhang, B., Yue, X., Denœux, T., & Yue, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123739 · Cited by 15

GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images

Singh, A., Yang, L., & Levine, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.623 · Cited by 17

Domain-Shared Group-Sparse Dictionary Learning for Unsupervised Domain Adaptation

Yang, B., Ma, A., & Yuen, P. C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12227 · Cited by 14

Unsupervised Domain Adaptation with Sphere Retracting Transformation

Fang, Z., Jie Lu, B., Liu, F., & Zhang, G. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851777 · Cited by 14

Particle swarm optimization based parameter selection technique for unsupervised discriminant analysis in transfer learning framework

Sanodiya, R. K., Mathew, J., Saha, S., & Tripathi, P. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01710-7 · Cited by 14

A particle swarm optimization-based feature selection for unsupervised transfer learning

Sanodiya, R. K., Tiwari, M., Mathew, J., Saha, S., & Saha, S. (2020) · Soft Computing · doi:10.1007/s00500-020-05105-1 · Cited by 16

Indirect Invisible Poisoning Attacks on Domain Adaptation

Wu, J., & He, J. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467214 · Cited by 14

Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection

Xu, J.-W., Zhang, Q.-W., Yu, Y., Zhao, R., Bian, X.-Z., Liu, X.-Q., Wang, J., Ge, Z., & Qian, D. (2021) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2021.106576 · Cited by 18

Local and Global Alignments for Generalizable Sensor-Based Human Activity Recognition

Wang, L., Wang, J., & Chen, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747363 · Cited by 14

Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation

Braytee, A., Naji, M., & Kennedy, P. J. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3163114 · Cited by 14

Transferable regularization and normalization: Towards transferable feature learning for unsupervised domain adaptation

Zhang, C., & Zhang, J. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.083 · Cited by 14

Instance Segmentation Model Evaluation and Rapid Deployment for Autonomous Driving Using Domain Differences

Guan, L., & Yuan, X. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3236626 · Cited by 14

An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

He, Y. D., Hu, M., Feng, K., & Jiang, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3034305 · Cited by 14

Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic SegmentationPreprint

Shen, T., Gong, D., Zhang, W., Shen, C., & Mei, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12282 · Cited by 17

From Small Data Modeling to Large Language Model Screening: A Dual‐Strategy Framework for Materials Intelligent Design

Yu, Y., Xiong, J., Wu, X. Z., & Qian, Q. (2024) · Advanced Science · doi:10.1002/advs.202403548 · Cited by 13

C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation

Sun, H., Chen, X., Wang, L., Liang, D., Liu, N., & Zhou, H. (2020) · Sensors · doi:10.3390/s20123606 · Cited by 13

Hard class rectification for domain adaptation

Zhang, Y., Jing, C., Lin, H., Chen, C., Huang, Y., Ding, X., & Zou, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107011 · Cited by 14

Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition

Huang, Y., Yang, X., Gao, J., & Xu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3078882 · Cited by 13

Spatially-Consistent Feature Matching and Learning for Heritage Image Analysis

Shen, X., Champenois, R., Ginosar, S., Pastrolin, I., Rousselot, M., Bounou, O., Monnier, T., Gidaris, S., Bougard, F., Raverdy, P.-G., et al. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01576-x · Cited by 16

Integrating Coupled Dictionary Learning and Distance Preserved Probability Distribution Adaptation for Multispectral–Hyperspectral Image Collaborative Classification

Guo, B., Liu, T., & Gu, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3169216 · Cited by 13

The accuracy losing phenomenon in abrasive tool condition monitoring and a noval WMMC-JDA based data-driven method considered tool stochastic surface morphology

Liu, M., Gong, Y., Sun, J., Tang, B., Sun, Y., Zu, X., & Zhao, J. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110410 · Cited by 13

Improving Domain Adaptation Through Class Aware Frequency Transformation

Kumar, V., Patil, H., Lal, R., & Chakraborty, A. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01810-0 · Cited by 13

Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence

Teng, S. Y., van Nooten, C. C., van Doorn, J. M., Ottenbros, A. B., Huijbregts, M. A. J., & Jansen, J. J. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113662 · Cited by 13

Multi-Fault and Severity Diagnosis for Self-Organizing Networks Using Deep Supervised Learning and Unsupervised Transfer Learning

Chen, K.-F., Lee, M., Lin, C.-H., Yeh, W.-C., & Lee, T.-S. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3276313 · Cited by 13

Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

Banet, T., Smith, A. G., McGrail, R. K., McNear, D. H., & Poffenbarger, H. (2024) · The Plant Phenome Journal · doi:10.1002/ppj2.20094 · Cited by 13

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Hei, Z., Shi, Q., Fan, X., Qian, F., Kumar, A., Zhong, M., & Zhou, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad3c5e · Cited by 14

Smooth-Guided Implicit Data Augmentation for Domain Generalization

Wang, M., Liu, J., Luo, G., Wang, S., Wang, W. W., Lan, L., Wang, Y., & Nie, F. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3377439 · Cited by 14

A two-stream sign language recognition network based on keyframe extraction method

Liu, T., Tao, T., Zhao, Y., & Zhu, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124268 · Cited by 14

Long-term student performance prediction using learning ability self-adaptive algorithm

Ren, Y., & Yu, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01476-2 · Cited by 13

Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment

Chen, K., Liu, J., Wan, R., Lee, V. H.-F., Vardhanabhuti, V., Yan, H., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3409573 · Cited by 13

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring

Yuan, Z., Kerckhoffs, J., Li, H., Khan, J., Hoek, G., & Vermeulen, R. C. H. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c06144 · Cited by 13

Classwise Prototype-Guided Alignment Network for Cross-Scene Hyperspectral Image Classification

Xie, Z., Duan, P., Kang, X., Liu, W., & Shutao Li, A. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3431702 · Cited by 13

M3D: Manifold-Based Domain Adaptation With Dynamic Distribution for Non-Deep Transfer Learning in Cross-Subject and Cross-Session EEG-Based Emotion Recognition

Luo, T., Zhang, J., Qiu, Y., Zhang, L., Hu, Y., Yu, Z. L., & Liang, Z. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3580612 · Cited by 16

Two-dimensional subspace alignment for convolutional activations adaptation

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