924 citations · 5.3k across the 63 of their papers we have counts for
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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan +3
Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is…
Variational Approaches for Auto-Encoding Generative Adversarial Networks
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley +1
Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given…
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Marc G. Bellemare, Ivo Danihelka, Will Dabney +4
The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probab…
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria +2
We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densit…
Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees
Daniel Zoran, Balaji Lakshminarayanan, Charles Blundell
Nearest neighbor (kNN) methods have been gaining popularity in recent years in light of advances in hardware and efficiency of algorithms. There is a plethora of methods to choose…