Domain Generalization with Adversarial Feature Learning
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[1] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[2] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[3] Dong Xu,et al. Exploiting Low-Rank Structure from Latent Domains for Domain Generalization , 2014, ECCV.
[4] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[5] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[6] Cordelia Schmid,et al. Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.
[7] Wen Gao,et al. Region-Aware Reflection Removal With Unified Content and Gradient Priors , 2018, IEEE Transactions on Image Processing.
[8] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[9] Mengjie Zhang,et al. Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation , 2016, ECCV.
[10] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[11] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[12] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[13] Sivaraman Balakrishnan,et al. Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.
[14] Aaron C. Courville,et al. Adversarially Learned Inference , 2016, ICLR.
[15] Le Song,et al. A Hilbert Space Embedding for Distributions , 2007, Discovery Science.
[16] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[18] Ivor W. Tsang,et al. Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.
[19] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[20] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[21] Bernhard Schölkopf,et al. Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions , 2009, NIPS.
[22] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[23] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[24] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[26] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[27] 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.
[28] Wei Gao,et al. Multi-View Discriminant Transfer Learning , 2013, IJCAI.
[29] Antonio Torralba,et al. LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.
[30] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[31] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[32] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[33] Mengjie Zhang,et al. Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[34] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[35] Bernhard Schölkopf,et al. A Kernel Method for the Two-Sample-Problem , 2006, NIPS.
[36] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[37] Rémi Ronfard,et al. Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..
[38] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[39] 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.
[40] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .