Fetching the paper…
Reading the bibliography…
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence.
Fokker-planck equation
Risken, H. and Risken, H · 1996
Earlier work this paper cites.
The variational formulation of the fokker–planck equation
Jordan, R., Kinderlehrer, D., and Otto, F · 1998
Earlier work this paper cites.
Inferences for case-control and semiparametric two-sample density ratio models
Qin, J · 1998
Earlier work this paper cites.
The geometry of dissipative evolution equations: the porous medium equation
Otto, F · 2001
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Ambrosio, L., Gigli, N., and Savaré, G · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
On integral probability metrics, ϕ \phi -divergences and binary classification
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Schölkopf, B., and Lanckriet, G. R · 2009
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Density ratio estimation in machine learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
Cited alongside, same era.
Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
Cited alongside, same era.
Generative adversarial nets from a density ratio estimation perspective
Uehara, M., Sato, I., Suzuki, M., Nakayama, K., and Matsuo, Y · 2016
Cited alongside, same era.
Composite functional gradient learning of generative adversarial models
Johnson, R. and Zhang, T · 2018
Later among the works it cites.
Deep generative learning via variational gradient flow
Gao, Y., Jiao, Y., Wang, Y., Wang, Y., Yang, C., and Zhang, S · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Later among the works it cites.
Monte carlo gradient estimation in machine learning. arxiv e-prints, page
Mohamed, S., Rosca, M., Figurnov, M., and Mnih, A · 2019
Later among the works it cites.
Training neural networks for likelihood/density ratio estimation
Moustakides, G. V. and Basioti, K · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S · 2017
Cited alongside, same era.
Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Roeder, G., Wu, Y., and Duvenaud, D. K · 2017
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Later among the works it cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Later among the works it cites.
Refining deep generative models via discriminator gradient flow
Ansari, A. F., Ang, M. L., and Soh, H · 2021
Later among the works it cites.
Generalized energy based models
Arbel, M., Zhou, L., and Gretton, A · 2021
Later among the works it cites.
A survey on generative adversarial networks: Variants, applications, and training
Jabbar, A., Li, X., and Omar, B · 2021
Later among the works it cites.
On the convergence of gradient descent in gans: Mmd gan as a gradient flow
Mroueh, Y. and Nguyen, T · 2021
Later among the works it cites.
How to train your energy-based models
Song, Y. and Kingma, D. P · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
Later among the works it cites.
A neural tangent kernel perspective of gans
Franceschi, J.-Y., De Bézenac, E., Ayed, I., Chen, M., Lamprier, S., and Gallinari, P · 2022
Later among the works it cites.
Understanding diffusion models: A unified perspective
Luo, C · 2022
Later among the works it cites.