Adaptive batch normalization for practical domain adaptation

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

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 Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · 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

The Art of Computer Programming

Knuth, D. E. (1968) · Cited by 16,097 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works

The Art of Computer Programming

Whitesides, G. E. (1970) · Nuclear Science and Engineering · doi:10.13182/nse70-a19705 · Cited by 6,263 · see citing works

How transferable are features in deep neural networks?

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · 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

A kernel two-sample test

(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603 · 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

Unbiased look at dataset bias

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

Untitled work

(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

Return of Frustratingly Easy Domain Adaptation

Sun, B., Feng, J., & Saenko, K. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10306 · Cited by 2,004 · see citing works

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819 · see citing works

Improving predictive inference under covariate shift by weighting the log-likelihood function

Shimodaira, H. (2000) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(00)00115-4 · Cited by 1,678 · 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

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

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 separation networks

Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2016) · arXiv · doi:10.48550/arxiv.1608.06019 · Cited by 1,208 · 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

A Learned Representation For Artistic StylePreprint

Dumoulin, V., Shlens, J., & Kudlur, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07629 · Cited by 1,033 · see citing works

Visual Domain Adaptation: A survey of recent advances

Patel, V. M., Gopalan, R., Li, R., & Chellappa, R. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2347059 · Cited by 883 · see citing works

Undoing the Damage of Dataset Bias

Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_12 · Cited by 482 · see citing works

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

Gong, B., Grauman, K. L., & Sha, F. (2013) · Cited by 459 · see citing works

Unsupervised Domain Adaptation by Domain Invariant Projection

Baktashmotlagh, M., Harandi, M. T., Lovell, B. C., & Salzmann, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.100 · Cited by 456 · see citing works

Modulating early visual processing by languagePreprint

de Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., & Courville, A. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00683 · Cited by 384 · see citing works

Co-Training for Domain Adaptation

Chen, M., Weinberger, K. Q., & Blitzer, J. C. (2011) · Cited by 374 · see citing works

Deep CORAL: Correlation Alignment for Deep Domain AdaptationPreprint

Sun, B., & Saenko, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.01719 · Cited by 361 · see citing works

Exploring the structure of a real-time, arbitrary neural artistic stylization network

Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., & Shlens, J. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.114 · Cited by 306 · see citing works

Simultaneous Deep Transfer Across Domains and Tasks

Hoffman, J., Tzeng, E., Darrell, T., & Saenko, K. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-58347-1_9 · Cited by 277 · see citing works

A Deeper Look at Dataset Bias

Tommasi, T., Patricia, N., Caputo, B., & Tuytelaars, T. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_2 · Cited by 245 · see citing works

Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach

Bergamo, A., & Torresani, L. (2010) · Cited by 214 · see citing works

Landmarks-based kernelized subspace alignment for unsupervised domain adaptation

Aljundi, R., Emonet, R., Muselet, D., & Sebban, M. (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.2015.7298600 · Cited by 133 · 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

A Deeper Look at Dataset Bias

Tommasi, T., Patricia, N., Barbara Caputo, B., & Tuytelaars, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_42 · Cited by 67 · see citing works

Semi-supervised transfer subspace for domain adaptation

Pereira, L. A. M., & Torres, R. D. S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.011 · Cited by 58 · see citing works

Joint cross-domain classification and subspace learning for unsupervised adaptation

Fernando, B., Tommasi, T., & Tuytelaars, T. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.07.009 · Cited by 50 · see citing works

A SVM-based model-transferring method for heterogeneous domain adaptation

Mozafari, A. S., & Jamzad, M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.009 · Cited by 45 · see citing works

Domain Adaptations for Computer Vision ApplicationsPreprint

Beijbom, O. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.4860 · Cited by 29 · see citing works

Sample selection for visual domain adaptation via sparse coding

Li, X., Fang, M., Zhang, J.-J., & Wu, J. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.03.009 · Cited by 10 · see citing works

Non-negative embedding for fully unsupervised domain adaptation

Redko, I., & Bennani, Y. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.03.016 · Cited by 9 · see citing works

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997 · 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

A survey of unsupervised deep domain adaptation

Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987 · see citing works

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109531 · Cited by 310 · see citing works

Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap

Park, T. H., & D’Amico, S. (2024) · Advances in Space Research · doi:10.1016/j.asr.2023.03.036 · Cited by 125 · see citing works

Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints

Wang, Z., Chen, M., Guo, Y., Li, Z., & Yu, Q. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3250385 · Cited by 64 · see citing works

MEMO: Test Time Robustness Via Adaptation and Augmentation

Zhang, M., Levine, S., & Finn, C. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2799 · Cited by 189 · see citing works

PVSPE: A pyramid vision multitask transformer network for spacecraft pose estimation

Yang, H., Xiao, X., Yao, M., Xiong, Y., Cui, H., & Fu, Y. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.05.011 · Cited by 41 · see citing works

A New Deep Transfer Learning Network for Fault Diagnosis of Rotating Machine Under Variable Working Conditions

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 · see citing works

Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

Zhang, C., Yu, Z., Wang, X., Chen, Z.-J., Deng, C., & Xie, S. Q. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106551 · Cited by 21 · see citing works

Interpretable Mesomorphic Networks for Tabular DataPreprint

Kadra, A., Arango, S. P., & Grabocka, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.13072 · Cited by 1 · see citing works

Transferable Normalization: Towards Improving Transferability of Deep Neural Networks

Wang, X., Jin, Y., Long, M., Wang, J., & Jordan, M. I. (2019) · neural information processing systems · Cited by 168 · see citing works

Two recent advances on normalization methods for deep neural network optimization

Zhang, L. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301751

Adversarial Robustness of Deep Learning: Theory, Algorithms, and ApplicationsPreprint

Ruan, W., Yi, X., & Huang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10451 · Cited by 3 · see citing works

Deep learning: Evolution and expansion

Wason, R. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.08.023 · Cited by 174 · see citing works