249 citations · 378 across the 4 of their papers we have counts for
10 papers
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg +4
The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models an…
End-to-End Adversarial Text-to-Speech
Jeff Donahue, Sander Dieleman, Mikołaj Bińkowski +2
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on th…
LOGAN: Latent Optimisation for Generative Adversarial Networks
Yan Wu, Jeff Donahue, David Balduzzi +2
Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dro…
High Fidelity Speech Synthesis with Adversarial Networks
Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman +5
Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in…
Adversarial Video Generation on Complex Datasets
Aidan Clark, Jeff Donahue, Karen Simonyan
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling…
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation…