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François Laviolette | Hugo Larochelle | Mario Marchand | Victor S. Lempitsky | Pascal Germain | Yaroslav Ganin | Evgeniya Ustinova | Hana Ajakan | H. Larochelle | V. Lempitsky | M. Marchand | Yaroslav Ganin | E. Ustinova | Hana Ajakan | Pascal Germain | François Laviolette
[1] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[2] Ronitt Rubinfeld,et al. Testing that distributions are close , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[3] Shai Ben-David,et al. Detecting Change in Data Streams , 2004, VLDB.
[4] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[5] John Blitzer,et al. Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.
[6] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[7] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[8] Hai Tao,et al. Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .
[9] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[10] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[11] Yishay Mansour,et al. Multiple Source Adaptation and the Rényi Divergence , 2009, UAI.
[12] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[13] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.
[14] Cordelia Schmid,et al. Multi-view object class detection with a 3D geometric model , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[15] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[16] Michael Goesele,et al. Back to the Future: Learning Shape Models from 3D CAD Data , 2010, BMVC.
[17] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[18] Lorenzo Bruzzone,et al. Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Qiang Yang,et al. Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning , 2010, ECML/PKDD.
[20] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[21] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Horst Bischof,et al. Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.
[23] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[24] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[25] Tapani Raiko,et al. The NIPS workshop on Deep Learning and Unsupervised Feature Learning , 2011 .
[26] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[27] Jürgen Schmidhuber,et al. Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.
[28] François Laviolette,et al. Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets , 2012, AISTATS.
[29] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[31] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[32] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[33] Alexander Yates,et al. Biased Representation Learning for Domain Adaptation , 2012, EMNLP.
[34] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[35] Chunxiao Liu,et al. POP: Person Re-identification Post-rank Optimisation , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Xiaogang Wang,et al. Person Re-identification by Salience Matching , 2013, 2013 IEEE International Conference on Computer Vision.
[37] Sumit Chopra,et al. DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .
[38] Brian C. Lovell,et al. Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.
[39] François Laviolette,et al. A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers , 2013, ICML.
[40] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[41] Xiaogang Wang,et al. Locally Aligned Feature Transforms across Views , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Venkatesh Saligrama,et al. Person Re-identification via Structured Prediction , 2014, ArXiv.
[43] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[44] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[45] Shaogang Gong,et al. Person Re-Identification , 2014 .
[46] Mehryar Mohri,et al. Domain adaptation and sample bias correction theory and algorithm for regression , 2014, Theor. Comput. Sci..
[47] Kate Saenko,et al. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains , 2014, BMVC.
[48] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[49] David Vázquez,et al. Virtual and Real World Adaptationfor Pedestrian Detection , 2014, IEEE Trans. Pattern Anal. Mach. Intell..
[50] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[51] Trevor Darrell,et al. One-Shot Adaptation of Supervised Deep Convolutional Models , 2013, ICLR.
[52] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[53] Stan Z. Li,et al. Deep Metric Learning for Practical Person Re-Identification , 2014, ArXiv.
[54] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[55] Ivan Laptev,et al. Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[56] François Laviolette,et al. Domain-Adversarial Neural Networks , 2014, ArXiv.
[57] Anton van den Hengel,et al. Learning to rank in person re-identification with metric ensembles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Ping Li,et al. Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs , 2015, IEEE Transactions on Image Processing.
[59] David Stutz,et al. Neural Codes for Image Retrieval , 2015 .
[60] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[61] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[62] Jian Dong,et al. Deep domain adaptation for describing people based on fine-grained clothing attributes , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[63] Xiaogang Wang,et al. Person Re-Identification by Saliency Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.