7 citations · 7 across the 1 of their papers we have counts for
2 papers
cs.LG2020★ 7 cited
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos, Grigorios Chrysos, Maja Pantic +1
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images. However, empirical studies have s…
cs.LG2019
PolyGAN: High-Order Polynomial Generators
Grigorios Chrysos, Stylianos Moschoglou, Yannis Panagakis +1
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions. Since their advent, numerous va…