Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation
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[1] Qilong Wang,et al. Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[3] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[4] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[5] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[7] Yaoliang Yu,et al. Analysis of Kernel Mean Matching under Covariate Shift , 2012, ICML.
[8] C. Villani. Optimal Transport: Old and New , 2008 .
[9] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[10] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[11] Zoubin Ghahramani,et al. Training generative neural networks via Maximum Mean Discrepancy optimization , 2015, UAI.
[12] Philip H. S. Torr,et al. Multi-agent Diverse Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[14] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[15] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[16] Zhen Wang,et al. Multi-class Generative Adversarial Networks with the L2 Loss Function , 2016, ArXiv.
[17] Jian Shen,et al. Wasserstein Distance Guided Representation Learning for Domain Adaptation , 2017, AAAI.
[18] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[19] Ian J. Wassell,et al. Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Yang Yu,et al. Mixture of GANs for Clustering , 2018, IJCAI.
[21] Gunnar Rätsch,et al. Clustering Meets Implicit Generative Models , 2018, ICLR.
[22] Trung Le,et al. MGAN: Training Generative Adversarial Nets with Multiple Generators , 2018, ICLR.
[23] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[24] Thomas M. Cover,et al. Elements of Information Theory , 2005 .