7 citations · 7 across the 1 of their papers we have counts for
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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…
Deep Polynomial Neural Networks
Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. T…
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…