Fetching the paper…
Reading the bibliography…
Generative Adversarial Networks (GANs), though powerful, is hard to train.
The fréchet distance between multivariate normal distributions
Dowson, D · 1982
Earlier work this paper cites.
Optimal design in random-effects regression models
Mentre, F · 1997
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Some results on d-optimal designs for nonlinear models with applications
Li, G · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A · 2011
Earlier work this paper cites.
Experiments: planning, analysis, and optimization
Wu, C. J · 2011
Earlier work this paper cites.
On optimal designs for nonlinear models: a general and efficient algorithm
Yang, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P · 2014
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O · 2015
Earlier work this paper cites.
The representation and parametrization of orthogonal matrices
Shepard, R · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C · 2015
Cited alongside, same era.
Learning to protect communications with adversarial neural cryptography
Abadi, M · 2016
Cited alongside, same era.
Neural photo editing with introspective adversarial networks
Brock, A · 2016
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Ho, J · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T · 2016
Gans trained by a two time-scale update rule converge to a nash equilibrium
Heusel, M · 2017
Later among the works it cites.
Huang, L · 2017
Later among the works it cites.
Adversarial learning for neural dialogue generation
Li, J · 2017
Later among the works it cites.
Gradient descent gan optimization is locally stable
Nagarajan, V · 2017
Later among the works it cites.
Stabilizing training of generative adversarial networks through regularization
Roth, K · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T · 2016
Cited alongside, same era.
Arjovsky, M · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (gans)
Arora, S · 2017
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
Bartlett, P. L · 2017
Cited alongside, same era.
Wasserstein distributional robustness and regularization in statistical learning
Gao, R · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I · 2017
Cited alongside, same era.
On the effects of batch and weight normalization in generative adversarial networks
Xiang, S · 2017
Later among the works it cites.
On the discrimination-generalization tradeoff in gans
Zhang, P · 2017
Later among the works it cites.
Barratt, S · 2018
Closest in time.
Learning towards minimum hyperspherical energy
Liu, W · 2018
Closest in time.
Spectral normalization for generative adversarial networks
Miyato, T · 2018
Closest in time.
cgans with projection discriminator
Miyato, T · 2018
Closest in time.
Generative image inpainting with contextual attention
Yu, J · 2018
Closest in time.