Center transfer for supervised domain adaptation
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K. Choi | Xiuyu Huang | Huaidong Zhang | Nan Zhou | W. Pedrycz | Jian Huang
[1] Jun Sun,et al. PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations , 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
[2] Zhedong Zheng,et al. Soft Person Reidentification Network Pruning via Blockwise Adjacent Filter Decaying , 2021, IEEE Transactions on Cybernetics.
[3] Qunsheng Ruan,et al. Cross-subject EEG emotion classification based on few-label adversarial domain adaption , 2021, Expert Syst. Appl..
[4] Xiaojun Jing,et al. Supervised Domain Adaptation for Few-Shot Radar-Based Human Activity Recognition , 2021, IEEE Sensors Journal.
[5] Jinan Fiaidhi,et al. Few Shot Learning of COVID-19 Classification Based on Sequential and Pretrained Models: A Thick Data Approach , 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC).
[6] Ankit Singh,et al. CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation , 2021, NeurIPS.
[7] Daniel Kuhn,et al. Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts , 2021, ICML.
[8] Bingbing Liu,et al. LiDAR few-shot domain adaptation via integrated CycleGAN and 3D object detector with joint learning delay , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[9] A. Darzi,et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis , 2021, npj Digital Medicine.
[10] Yuhong Guo,et al. Domain Adaptation With Neural Embedding Matching , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[11] Alexandros Iosifidis,et al. Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol , 2020, IEEE Transactions on Image Processing.
[12] Zewen Li,et al. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[13] Omar Ali Sheikh-Omar,et al. Supervised Domain Adaptation using Graph Embedding , 2020, 2020 25th International Conference on Pattern Recognition (ICPR).
[14] Masashi Sugiyama,et al. Few-shot Domain Adaptation by Causal Mechanism Transfer , 2020, ICML.
[15] Gurumurthy Swaminathan,et al. d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Trevor Darrell,et al. Semi-Supervised Domain Adaptation via Minimax Entropy , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[17] Tiago H. Falk,et al. Deep learning-based electroencephalography analysis: a systematic review , 2019, Journal of neural engineering.
[18] Diane J. Cook,et al. A Survey of Unsupervised Deep Domain Adaptation , 2018, ACM Trans. Intell. Syst. Technol..
[19] Mei Wang,et al. Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.
[20] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Fatih Murat Porikli,et al. Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Yu Qiao,et al. A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.
[24] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[25] Narayanan Chatapuram Krishnan,et al. Supervised Heterogeneous Domain Adaptation via Random Forests , 2016, IJCAI.
[26] Trevor Darrell,et al. Simultaneous Deep Transfer Across Domains and Tasks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[27] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[28] Carlos Busso,et al. Supervised domain adaptation for emotion recognition from speech , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[29] Tinne Tuytelaars,et al. Joint cross-domain classification and subspace learning for unsupervised adaptation , 2014, Pattern Recognit. Lett..
[30] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[31] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[32] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[33] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[35] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[36] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[37] Xin-Yi Tong. A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning , 2021, NeurIPS.
[38] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[39] Ivor W. Tsang,et al. Heterogeneous Domain Adaptation for Multiple Classes , 2014, AISTATS.
[40] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[41] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[42] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[43] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.