Fast Generalized Distillation for Semi-Supervised Domain Adaptation
暂无分享,去创建一个
[1] Daumé,et al. Frustratingly Easy Semi-Supervised Domain Adaptation , 2010 .
[2] Trevor Darrell,et al. Simultaneous Deep Transfer Across Domains and Tasks , 2015, ICCV.
[3] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[4] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[5] Rich Caruana,et al. Do Deep Nets Really Need to be Deep? , 2013, NIPS.
[6] Xiaogang Wang,et al. Face Model Compression by Distilling Knowledge from Neurons , 2016, AAAI.
[7] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[8] Chong-Wah Ngo,et al. Semi-supervised Domain Adaptation with Subspace Learning for visual recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Bernhard Schölkopf,et al. Unifying distillation and privileged information , 2015, ICLR.
[10] Donald A. Adjeroh,et al. Information Bottleneck Learning Using Privileged Information for Visual Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Christoph H. Lampert,et al. Learning to Rank Using Privileged Information , 2013, 2013 IEEE International Conference on Computer Vision.
[14] Ivor W. Tsang,et al. Visual Event Recognition in Videos by Learning from Web Data , 2012, IEEE Trans. Pattern Anal. Mach. Intell..
[15] Ivor W. Tsang,et al. Learning with Augmented Features for Heterogeneous Domain Adaptation , 2012, ICML.
[16] Yoshua Bengio,et al. FitNets: Hints for Thin Deep Nets , 2014, ICLR.
[17] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Matthew Richardson,et al. Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? , 2016, ArXiv.
[19] Gavin C. Cawley,et al. Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[20] Trevor Darrell,et al. Semi-supervised Domain Adaptation with Instance Constraints , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Barbara Caputo,et al. Learning Categories From Few Examples With Multi Model Knowledge Transfer , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Ivor W. Tsang,et al. Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.
[23] Rauf Izmailov,et al. Learning using privileged information: similarity control and knowledge transfer , 2015, J. Mach. Learn. Res..
[24] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.