Discriminative and Geometry-Aware Unsupervised Domain Adaptation
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Liming Chen | Shiqiang Hu | Xiaofang Wang | Lingkun Luo | Ying Lu | Shiqiang Hu | Liming Chen | Ying Lu | Lingkun Luo | Xiaofang Wang
[1] Edward R. Dougherty,et al. Optimal Bayesian Transfer Learning , 2018, IEEE Transactions on Signal Processing.
[2] Liming Chen,et al. Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation , 2017, ArXiv.
[3] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[4] Philip S. Yu,et al. Visual Domain Adaptation with Manifold Embedded Distribution Alignment , 2018, ACM Multimedia.
[5] Pascal Fua,et al. Beyond Sharing Weights for Deep Domain Adaptation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Juergen Gall,et al. Open Set Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[7] Yuxing Tang,et al. Close Yet Distinctive Domain Adaptation , 2017, ArXiv.
[8] M. Fortin,et al. Augmented Lagrangian methods : applications to the numerical solution of boundary-value problems , 1983 .
[9] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[10] Dong Liu,et al. Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[11] Muhammad Uzair,et al. Blind Domain Adaptation With Augmented Extreme Learning Machine Features , 2017, IEEE Transactions on Cybernetics.
[12] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[13] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[14] Mengjie Zhang,et al. Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Mehrtash Tafazzoli Harandi,et al. Learning an Invariant Hilbert Space for Domain Adaptation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Philip S. Yu,et al. Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Dacheng Tao,et al. Bregman Divergence-Based Regularization for Transfer Subspace Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[18] Sunita Sarawagi,et al. Domain Adaptation of Conditional Probability Models Via Feature Subsetting , 2007, PKDD.
[19] Trevor Darrell,et al. Semi-supervised Domain Adaptation with Instance Constraints , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[20] SalzmannMathieu,et al. Distribution-matching embedding for visual domain adaptation , 2016 .
[21] Philip S. Yu,et al. Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[22] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[23] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[24] Shai Ben-David,et al. Detecting Change in Data Streams , 2004, VLDB.
[25] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[26] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[27] Ming Shao,et al. Generalized Transfer Subspace Learning Through Low-Rank Constraint , 2014, International Journal of Computer Vision.
[28] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[29] Yuxing Tang,et al. Visual and Semantic Knowledge Transfer for Large Scale Semi-Supervised Object Detection , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Silvio Savarese,et al. Adversarial Feature Augmentation for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[32] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[33] Huchuan Lu,et al. Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[34] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[35] Qiang Yang,et al. Transfer Learning via Dimensionality Reduction , 2008, AAAI.
[36] Yu-Chiang Frank Wang,et al. Unsupervised Domain Adaptation With Label and Structural Consistency , 2016, IEEE Transactions on Image Processing.
[37] Jiawei Han,et al. Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[38] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[40] Rama Chellappa,et al. Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.
[41] Sang Uk Lee,et al. Learning full pairwise affinities for spectral segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[42] Xuelong Li,et al. Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation , 2016, IEEE Transactions on Image Processing.
[43] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[44] Jing Zhang,et al. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[46] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[47] Fanjiang Xu,et al. Cross-Domain Metric Learning Based on Information Theory , 2014, AAAI.
[48] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[49] Silvio Savarese,et al. Learning Transferrable Representations for Unsupervised Domain Adaptation , 2016, NIPS.
[50] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[51] Chuan Chen,et al. Learning Semantic Representations for Unsupervised Domain Adaptation , 2018, ICML.
[52] Gabriela Csurka,et al. Domain Adaptation for Visual Applications: A Comprehensive Survey , 2017, ArXiv.
[53] Yun Fu,et al. Robust Transfer Metric Learning for Image Classification , 2017, IEEE Transactions on Image Processing.
[54] Motoaki Kawanabe,et al. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.
[55] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[56] Tatsuya Harada,et al. Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.
[57] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[58] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[59] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..