Wasserstein Generative Adversarial Network
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[1] E. Tabak,et al. A Family of Nonparametric Density Estimation Algorithms , 2013 .
[2] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[3] Otmar Hilliges,et al. Guiding InfoGAN with Semi-supervision , 2017, ECML/PKDD.
[4] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[5] O. Bousquet,et al. From optimal transport to generative modeling: the VEGAN cookbook , 2017, 1705.07642.
[6] C. Villani. Optimal Transport: Old and New , 2008 .
[7] I. Good. Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables , 1963 .
[8] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[9] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[10] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[11] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[12] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[13] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[14] Stefano Ermon,et al. Flow-GAN: Bridging implicit and prescribed learning in generative models , 2017, ArXiv.
[15] Yoshua Bengio,et al. NICE: Non-linear Independent Components Estimation , 2014, ICLR.
[16] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[17] Vaibhava Goel,et al. McGan: Mean and Covariance Feature Matching GAN , 2017, ICML.
[18] Ravi Kiran Sarvadevabhatla,et al. DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Sebastian Nowozin,et al. Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks , 2017, ICML.
[20] Marc G. Bellemare,et al. The Cramer Distance as a Solution to Biased Wasserstein Gradients , 2017, ArXiv.
[21] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[22] Anton van den Hengel,et al. Infinite Variational Autoencoder for Semi-Supervised Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[24] Marco Cuturi,et al. Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.
[25] Matthias Bethge,et al. A note on the evaluation of generative models , 2015, ICLR.
[26] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[27] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[28] Gabriel Peyré,et al. Sinkhorn-AutoDiff: Tractable Wasserstein Learning of Generative Models , 2017 .
[29] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[30] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[31] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[32] E. Tabak,et al. DENSITY ESTIMATION BY DUAL ASCENT OF THE LOG-LIKELIHOOD ∗ , 2010 .