Learning Transferrable Representations for Unsupervised Domain Adaptation
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Silvio Savarese | Ashutosh Saxena | Ozan Sener | Hyun Oh Song | S. Savarese | Ashutosh Saxena | Ozan Sener
[1] Vladimir Kolmogorov,et al. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[3] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[4] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[5] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Avrim Blum,et al. Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.
[7] Zoubin Ghahramani,et al. Learning from labeled and unlabeled data with label propagation , 2002 .
[8] Barbara Caputo,et al. Frustratingly Easy NBNN Domain Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[9] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[10] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[11] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[12] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[13] Mikhail Belkin,et al. Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.
[14] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[15] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[16] Christoph H. Lampert,et al. CoConut: Co-Classification with Output Space Regularization , 2014, BMVC.
[17] Geoffrey E. Hinton,et al. Zero-shot Learning with Semantic Output Codes , 2009, NIPS.
[18] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Sumit Chopra,et al. DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .
[21] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[22] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[23] Alexander Gammerman,et al. Learning by Transduction , 1998, UAI.
[24] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[26] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[27] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[28] Bernt Schiele,et al. Transfer Learning in a Transductive Setting , 2013, NIPS.
[29] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[30] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[31] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[32] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[33] Ruth Urner,et al. Active Nearest Neighbors in Changing Environments , 2015, ICML.
[34] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..