Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
暂无分享,去创建一个
Mengjie Zhang | W. Bastiaan Kleijn | Muhammad Ghifary | David Balduzzi | D. Balduzzi | Muhammad Ghifary | W. Kleijn | Mengjie Zhang
[1] Ye Xu,et al. Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias , 2013, 2013 IEEE International Conference on Computer Vision.
[2] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[3] Trevor Darrell,et al. Efficient Learning of Domain-invariant Image Representations , 2013, ICLR.
[4] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[5] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.
[6] Antonio Torralba,et al. LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.
[7] James J. Jiang. A Literature Survey on Domain Adaptation of Statistical Classifiers , 2007 .
[8] Ivor W. Tsang,et al. Visual Event Recognition in Videos by Learning from Web Data , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Shai Ben-David,et al. Detecting Change in Data Streams , 2004, VLDB.
[10] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[11] Mehryar Mohri,et al. Domain adaptation and sample bias correction theory and algorithm for regression , 2014, Theor. Comput. Sci..
[12] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[13] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[14] Luc Van Gool,et al. Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..
[15] Ingo Steinwart,et al. On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..
[16] Mengjie Zhang,et al. Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[17] Hal Daumé,et al. Frustratingly Easy Domain Adaptation , 2007, ACL.
[18] Barbara Caputo,et al. Frustratingly Easy NBNN Domain Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[19] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[20] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[21] G. Griffin,et al. Caltech-256 Object Category Dataset , 2007 .
[22] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[23] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[24] Jianmin Wang,et al. Transfer Learning with Graph Co-Regularization , 2012, IEEE Transactions on Knowledge and Data Engineering.
[25] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[26] Ivor W. Tsang,et al. Domain Adaptation from Multiple Sources : A Domain-Dependent Regularization Approach , 2012 .
[27] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[29] Cordelia Schmid,et al. Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.
[30] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[31] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[32] Yuesheng Xu,et al. Universal Kernels , 2006, J. Mach. Learn. Res..
[33] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[34] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[35] Rong Yan,et al. Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.
[36] Vishal M. Patel,et al. Joint Hierarchical Domain Adaptation and Feature Learning , 2013 .
[37] Philip S. Yu,et al. Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[39] Mengjie Zhang,et al. Domain Adaptive Neural Networks for Object Recognition , 2014, PRICAI.
[40] K. Johana,et al. Benchmarking Least Squares Support Vector Machine Classifiers , 2022 .
[41] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[42] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[43] Matthias W. Seeger,et al. Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.
[44] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[45] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[46] Jianguo Zhang,et al. The PASCAL Visual Object Classes Challenge , 2006 .
[47] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[48] B. Scholkopf,et al. Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[49] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[50] Bo Geng,et al. DAML: Domain Adaptation Metric Learning , 2011, IEEE Transactions on Image Processing.
[51] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[52] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[53] Alexei A. Efros,et al. Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.
[54] Antonio Torralba,et al. Exploiting hierarchical context on a large database of object categories , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[55] J. Hoffmann-jorgensen. Probability in Banach Space , 1977 .
[56] Philip S. Yu,et al. Transfer Sparse Coding for Robust Image Representation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[57] John Blitzer,et al. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.
[58] Sumit Chopra,et al. DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .
[59] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[60] Bernhard Schölkopf,et al. Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..
[61] Trevor Darrell,et al. Continuous Manifold Based Adaptation for Evolving Visual Domains , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[62] Alexei A. Efros,et al. Undoing the Damage of Dataset Bias , 2012, ECCV.
[63] Rama Chellappa,et al. Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.
[64] Antonio Criminisi,et al. Object categorization by learned universal visual dictionary , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[65] Brian C. Lovell,et al. Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.
[66] Rama Chellappa,et al. Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.
[67] Colin McDiarmid,et al. Surveys in Combinatorics, 1989: On the method of bounded differences , 1989 .
[68] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[69] Kate Saenko,et al. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains , 2014, BMVC.
[70] Andrea Vedaldi,et al. Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.
[71] Le Song,et al. A Hilbert Space Embedding for Distributions , 2007, Discovery Science.
[72] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[73] Cordelia Schmid,et al. Dataset Issues in Object Recognition , 2006, Toward Category-Level Object Recognition.
[74] Ivor W. Tsang,et al. Domain Transfer Multiple Kernel Learning , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[75] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[76] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[77] Vladimir Pavlovic,et al. Central Subspace Dimensionality Reduction Using Covariance Operators , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[78] John Shawe-Taylor,et al. Smooth Operators , 2013, ICML.
[79] Tara N. Sainath,et al. Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[80] Bernhard Schölkopf,et al. A Kernel Method for the Two-Sample-Problem , 2006, NIPS.
[81] Gilles Blanchard,et al. Generalizing from Several Related Classification Tasks to a New Unlabeled Sample , 2011, NIPS.
[82] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[83] Dong Xu,et al. Exploiting Low-Rank Structure from Latent Domains for Domain Generalization , 2014, ECCV.
[84] Le Song,et al. A Hilbert Space Embedding for Distributions , 2007, IFIP Working Conference on Database Semantics.
[85] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[86] Ivor W. Tsang,et al. Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.
[87] Brian C. Lovell,et al. Domain Adaptation on the Statistical Manifold , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[88] H. Shimodaira,et al. Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .
[89] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[90] Rémi Ronfard,et al. Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..
[91] John Blitzer,et al. Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.
[92] Trevor Darrell,et al. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.
[93] Ivor W. Tsang,et al. Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[94] Rama Chellappa,et al. Generalized Domain-Adaptive Dictionaries , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.