Fetching the paper…
Reading the bibliography…
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation.
Elements of Information Theory
T. Cover · 2005
Earlier work this paper cites.
Applied Nonlinear Analysis
J. Aubin and I. Ekeland · 2006
Earlier work this paper cites.
Weak convergence of measures
V. Bogachev · 2007
Earlier work this paper cites.
On the geometry of the space of probability measures endowed with the quadratic Optimal Transport distance
N. Gigli · 2008
Earlier work this paper cites.
Optimal Transport: Old And New
C. Villani · 2009
Earlier work this paper cites.
Inverse problems: A Bayesian perspective
A. M. Stuart · 2010
Earlier work this paper cites.
The MNIST database of handwritten digit images for machine learning research
L. Deng · 2012
Earlier work this paper cites.
Generative Adversarial Nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
A hitchhikers guide to wasserstein distances
G. Basso · 2015
Earlier work this paper cites.
Optimal Transport for applied mathematicians
F. Santambrogio · 2015
Earlier work this paper cites.
J. Adler and O. Öktem · 2018
Earlier work this paper cites.
Learning generative models with Sinkhorn divergences
A. Genevay, G. Peyre, and M. Cuturi · 2018
Earlier work this paper cites.
Sobolev GAN
Y. Mroueh, C.-L. Li, T. Sercu, A. Raj, and Y. Cheng · 2018
Cited alongside, same era.
Guided image generation with conditional invertible neural networks
L. Ardizzone, C. Lüth, J. Kruse, C. Rother, and U. Köthe · 2019
Cited alongside, same era.
Interpolating between Optimal Transport and MMD using Sinkhorn divergences
J. Feydy, T. Séjourné, F.-X. Vialard, S.-i. Amari, A. Trouvé, and G. Peyré · 2019
Cited alongside, same era.
Computational Optimal Transport: With applications to data science
G. Peyré and M. Cuturi · 2019
Cited alongside, same era.
Learning likelihoods with conditional normalizing flows
C. Winkler, D. Worrall, E. Hoogeboom, and M. Welling · 2019
Cited alongside, same era.
Generalized normalizing flows via Markov chains
P. Hagemann, J. Hertrich, and G. Steidl · 2022
Later among the works it cites.
Stochastic normalizing flows for inverse problems: A Markov chains viewpoint
P. Hagemann, J. Hertrich, and G. Steidl · 2022
Later among the works it cites.
Cycle class consistency with distributional Optimal Transport and knowledge distillation for unsupervised domain adaptation
T. Nguyen, V. Nguyen, T. Le, H. Zhao, Q. H. Tran, and D. Phung · 2022
Later among the works it cites.
Optimal Transport for conditional domain matching and label shift
A. Rakotomamonjy, R. Flamary, G. Gasso, M. E. Alaya, M. Berar, and N. Courty · 2022
Later among the works it cites.
Collapse by conditioning: Training class-conditional GANs with limited data
M. Shahbazi, M. Danelljan, D. P. Paudel, and L. V. Gool · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification
M. Zheng, T. Li, R. Zhu, Y. Tang, M. Tang, L. Lin, and Z. Ma · 2020
Cited alongside, same era.
Lectures on Optimal Transport
L. Ambrosio, E. Brué, and D. Semola · 2021
Cited alongside, same era.
Pot: Python Optimal Transport
R. Flamary, N. Courty, A. Gramfort, M. Z. Alaya, A. Boisbunon, S. Chambon, L. Chapel, A. Corenflos, K. Fatras, N. Fournier, et al · 2021
Cited alongside, same era.
Benchmarking invertible architectures on inverse problems
J. Kruse, L. Ardizzone, C. Rother, and U. Köthe · 2021
Cited alongside, same era.
Wasserstein generative learning of conditional distribution
S. Liu, X. Zhou, Y. Jiao, and J. Huang · 2021
Cited alongside, same era.
About exchanging expectation and supremum for conditional Wasserstein GANs
J. Martin · 2021
Cited alongside, same era.
Data driven conditional Optimal Transport
E. G. Tabak, G. Trigila, and W. Zhao · 2021
Cited alongside, same era.
F. Altekrüger, P. Hagemann, and G. Steidl · 2023
Closest in time.
Data interpolants – that’s what discriminators in higher-order gradient-regularized GANs are
S. Asokan and C. S. Seelamantula · 2023
Closest in time.
Nonparametric generative modeling with conditional sliced-wasserstein flows
C. Du, T. Li, T. Pang, S. Yan, and M. Lin · 2023
Closest in time.
Wasserstein geodesic generator for conditional distributions
Y. geun Kim, K. Lee, Y. Choi, J.-H. Won, and M. C. Paik · 2023
Closest in time.
Posterior sampling based on gradient flows of the MMD with negative distance kernel
P. Hagemann, J. Hertrich, F. Altekrüger, R. Beinert, J. Chemseddine, and G. Steidl · 2023
Closest in time.
Conditional wasserstein generator
Y.-g. Kim, K. Lee, and M. C. Paik · 2023
Closest in time.
Empirical Optimal Transport between conditional distributions
P. Manupriya, R. K. Das, S. Biswas, S. Chandhok, and S. N. Jagarlapudi · 2023
Closest in time.
D. Ray, J. Murgoitio-Esandi, A. Dasgupta, and A. A. Oberai · 2023
Closest in time.