Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
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[1] Tatsuya Harada,et al. Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.
[2] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[3] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[4] Deli Zhao,et al. Scalable Gaussian Process Regression Using Deep Neural Networks , 2015, IJCAI.
[5] Anton Konushin,et al. Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data , 2013, ACIVS.
[6] Yan Huang,et al. Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[8] Edwin Lughofer,et al. Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning , 2017, ICLR.
[9] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[10] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[11] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[12] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[13] Daniel Cremers,et al. Associative Domain Adaptation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[14] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[15] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[16] Barbara Caputo,et al. Boosting Domain Adaptation by Discovering Latent Domains , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Xiaojin Zhu,et al. Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[18] David J. Kriegman,et al. Image to Image Translation for Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Edwin V. Bonilla,et al. Scalable Inference for Gaussian Process Models with Black-Box Likelihoods , 2015, NIPS.
[20] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[21] Vladimir Pavlovic,et al. PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[22] Pedro H. O. Pinheiro,et al. Unsupervised Domain Adaptation with Similarity Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[24] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[25] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[26] Shiguang Shan,et al. Duplex Generative Adversarial Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Jan Kautz,et al. Unsupervised Image-to-Image Translation Networks , 2017, NIPS.
[28] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[29] Johannes Stallkamp,et al. The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.
[30] Pascal Fua,et al. Residual Parameter Transfer for Deep Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.
[32] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[33] Alexei A. Efros,et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Brian C. Lovell,et al. Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.
[35] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[37] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Konstantinos G. Derpanis,et al. The Bhattacharyya Measure , 2008 .
[39] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[40] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[41] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[42] Motoaki Kawanabe,et al. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.
[43] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[44] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[45] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[47] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[48] Carlos D. Castillo,et al. Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Vishal Patel,et al. Domain Adaptive Subspace Clustering , 2016, BMVC.
[50] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[51] Mengjie Zhang,et al. Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation , 2016, ECCV.
[52] Philip S. Yu,et al. Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[53] Yu-Chiang Frank Wang,et al. Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[54] Gabriela Csurka,et al. A Comprehensive Survey on Domain Adaptation for Visual Applications , 2017, Domain Adaptation in Computer Vision Applications.
[55] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[56] Quinn Jones,et al. Few-Shot Adversarial Domain Adaptation , 2017, NIPS.
[57] Andrew Gordon Wilson,et al. Deep Kernel Learning , 2015, AISTATS.
[58] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[60] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[61] Jing Zhang,et al. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Vishal M. Patel,et al. Adversarial domain adaptive subspace clustering , 2018, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).
[63] Qilong Wang,et al. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[65] Nicolas Courty,et al. Joint distribution optimal transportation for domain adaptation , 2017, NIPS.
[66] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[67] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[68] Zhuowen Tu,et al. Max-Margin Multiple-Instance Dictionary Learning , 2013, ICML.
[69] Lior Wolf,et al. One-Sided Unsupervised Domain Mapping , 2017, NIPS.
[70] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[71] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[72] Shiguang Shan,et al. Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[73] Shih-Fu Chang,et al. Deep Transfer Network: Unsupervised Domain Adaptation , 2015, ArXiv.
[74] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[75] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.