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Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation.
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H. G. Kellerer · 1984
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Optimal maps for the multidimensional monge-kantorovich problem
W. Gangbo and A. Świȩch · 1998
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Envelope theorems for arbitrary choice sets
P. Milgrom and I. Segal · 2002
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Optimal Transport: Old and New
C. Villani · 2008
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Optimal Transport: Old and New
C. Villani · 2008
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Learning with a wasserstein loss
C. Frogner, C. Zhang, H. Mobahi, M. Araya, and T. A. Poggio · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Optimal transport for applied mathematicians
F. Santambrogio · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe, and C. Szegedy · 2015
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Optimal transport for applied mathematicians
F. Santambrogio · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep Multi-scale Video Prediction beyond Mean Square Error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang · 2017
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On the flatness of loss surface for two-layered relu networks
J. Cao, Q. Wu, Y. Yan, L. Wang, and M. Tan · 2017
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Controlling perceptual factors in neural style transfer
L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, and E. Shechtman · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Arbitrary facial attribute editing: Only change what you want
Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Learning to discover cross-domain relations with generative adversarial networks
T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley · 2017
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Stabilizing training of generative adversarial networks through regularization
K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Z. Yi, H. R. Zhang, P. Tan, and M. Gong · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Toward multimodal image-to-image translation
J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman · 2017
On the regularization of wasserstein gans
H. Petzka, A. Fischer, and D. Lukovnikov · 2018
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On the convergence and robustness of training gans with regularized optimal transport
M. Sanjabi, J. Ba, M. Razaviyayn, and J. D. Lee · 2018
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Learning semantic representations for unsupervised domain adaptation
S. Xie, Z. Zheng, L. Chen, and C. Chen · 2018
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Online adaptive asymmetric active learning for budgeted imbalanced data
Y. Zhang, P. Zhao, J. Cao, W. Ma, J. Huang, Q. Wu, and M. Tan · 2018
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Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 2018
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Adversarial learning with local coordinate coding
J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, and M. Tan · 2018
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Cited alongside, same era.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
On the flatness of loss surface for two-layered relu networks
J. Cao, Q. Wu, Y. Yan, L. Wang, and M. Tan · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
Banach wasserstein gan
J. Adler and S. Lunz · 2018
Cited alongside, same era.
Augmented cyclegan: Learning many-to-many mappings from unpaired data
A. Almahairi, S. Rajeshwar, A. Sordoni, P. Bachman, and A. Courville · 2018
Cited alongside, same era.
Adversarial learning with local coordinate coding
J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, and M. Tan · 2018
Cited alongside, same era.
Later among the works it cites.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo · 2018
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Learning generative models with sinkhorn divergences
A. Genevay, G. Peyre, and M. Cuturi · 2018
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Dual reconstruction nets for image super-resolution with gradient sensitive loss
Y. Guo, Q. Chen, J. Chen, J. Huang, Y. Xu, J. Cao, P. Zhao, and M. Tan · 2018
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Double Forward Propagation for Memorized Batch Normalization
Y. Guo, Q. Wu, C. Deng, J. Chen, and M. Tan · 2018
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A unified feature disentangler for multi-domain image translation and manipulation
A. H. Liu, Y.-C. Liu, Y.-Y. Yeh, and Y.-C. F. Wang · 2018
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A two-step computation of the exact gan wasserstein distance
H. Liu, G. Xianfeng, and D. Samaras · 2018
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Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
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Auto-embedding generative adversarial networks for high resolution image synthesis
Y. Guo, Q. Chen, J. Chen, Q. Wu, Q. Shi, and M. Tan · 2019
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Nat: Neural architecture transformer for accurateand compact architectures
Y. Guo, Y. Zheng, M. Tan, Q. Chen, J. Chen, P. Zhao, and J. Huang · 2019
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Evolutionary generative adversarial networks
C. Wang, C. Xu, X. Yao, and D. Tao · 2019
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Oversampling for imbalanced data via optimal transport
Y. Yan, M. Tan, Y. Xu, J. Cao, M. Ng, H. Min, and Q. Wu · 2019
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Breaking winner-takes-all: Iterative-winners-out networks for weakly supervised temporal action localization
R. Zeng, C. Gan, P. Chen, W. Huang, Q. Wu, and M. Tan · 2019
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Graph convolutional networks for temporal action localization
R. Zeng, W. Huang, M. Tan, Y. Rong, P. Zhao, J. Huang, and C. Gan · 2019
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Multi-marginal Wasserstein GAN
J. Cao, L. Mo, Y. Zhang, K. Jia, C. Shen, and M. Tan · 2019
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Learning joint wasserstein auto-encoders for joint distribution matching, 2019
J. Cao, Y. Guo, L. Mo, P. Zhao, J. Huang, and M. Tan · 2019
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Oversampling for imbalanced data via optimal transport
Y. Yan, M. Tan, Y. Xu, J. Cao, M. Ng, H. Min, and Q. Wu · 2019
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From whole slide imaging to microscopy: Deep microscopy adaptation network for histopathology cancer image classification
Y. Zhang, H. Chen, Y. Wei, P. Zhao, J. Cao, X. Fan, X. Lou, H. Liu, J. Hou, X. Han, J. Yao, Q. Wu, M. Tan, and J. Huang · 2019
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