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[1] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[2] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[3] Kenji Fukumizu,et al. Equivalence of distance-based and RKHS-based statistics in hypothesis testing , 2012, ArXiv.
[4] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[5] G. Griffin,et al. Caltech-256 Object Category Dataset , 2007 .
[6] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[7] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[8] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[9] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[10] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[11] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[12] Sivaraman Balakrishnan,et al. Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.
[13] Liang Ge,et al. Multi-source deep learning for information trustworthiness estimation , 2013, KDD.
[14] Mengjie Zhang,et al. Domain Adaptive Neural Networks for Object Recognition , 2014, PRICAI.
[15] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[16] Bernhard Schölkopf,et al. Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions , 2009, NIPS.
[17] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[18] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[19] Philip S. Yu,et al. Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[20] François Laviolette,et al. Domain-Adversarial Neural Networks , 2014, ArXiv.
[21] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[22] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[23] Trevor Darrell,et al. LSDA: Large Scale Detection through Adaptation , 2014, NIPS.
[24] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[27] Brian C. Lovell,et al. Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.
[28] Jeff G. Schneider,et al. Flexible Transfer Learning under Support and Model Shift , 2014, NIPS.
[29] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[30] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[33] Bernhard Schölkopf,et al. Domain Adaptation under Target and Conditional Shift , 2013, ICML.
[34] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.