Fetching the paper…
Reading the bibliography…
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.
Sur la distance de deux lois de probabilité
Fréchet, M · 1957
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 1957
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Multi-objective optimization using evolutionary algorithms , volume 16
Deb, K · 2001
Earlier work this paper cites.
Stochastic method for the solution of unconstrained vector optimization problems
Schäffler, S., Schultz, R., and Weinzierl, K · 2002
Earlier work this paper cites.
The measure of pareto optima applications to multi-objective metaheuristics
Fleischer, M · 2003
Earlier work this paper cites.
SMS-EMOA: Multiobjective selection based on dominated hypervolume
Beume, N., Naujoks, B., and Emmerich, M · 2007
Earlier work this paper cites.
Theory of the hypervolume indicator: optimal μ \mu -distributions and the choice of the reference point
Auger, A., Bader, J., Brockhoff, D., and Zitzler, E · 2009
Earlier work this paper cites.
HypE: An algorithm for fast hypervolume-based many-objective optimization
Bader, J. and Zitzler, E · 2011
Earlier work this paper cites.
Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications
Auger, A., Bader, J., Brockhoff, D., and Zitzler, E · 2012
Earlier work this paper cites.
Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
Désidéri, J.-A · 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
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2016
Cited alongside, same era.
Generative multi-adversarial networks
Durugkar, I., Gemp, I., and Mahadevan, S · 2016
BEGAN: boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., and Metz, L · 2017
Later among the works it cites.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Later among the works it cites.
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
Later among the works it cites.
PacGAN: The power of two samples in generative adversarial networks
Lin, Z., Khetan, A., Fanti, G., and Oh, S · 2017
Later among the works it cites.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Smolley, S. P · 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.
NIPS 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
Cited alongside, same era.
Single-solution hypervolume maximization and its use for improving generalization of neural networks
Miranda, C. S. and Zuben, F. J. V · 2016
Cited alongside, same era.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Neyshabur, B., Bhojanapalli, S., and Chakrabarti, A · 2017
Later among the works it cites.
VEEGAN: Reducing mode collapse in GANs using implicit variational learning
Srivastava, A., Valkoz, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
Later among the works it cites.
Online adaptative curriculum learning for gans
Doan, T., Monteiro, J., Albuquerque, I., Mazoure, B., Durand, A., Pineau, J., and Hjelm, R. D · 2018
Later among the works it cites.
The relativistic discriminator: a key element missing from standard gan
Jolicoeur-Martineau, A · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Later among the works it cites.
Gradient-based multiobjective optimization with uncertainties
Peitz, S. and Dellnitz, M · 2018
Later among the works it cites.