20 citations · 24 across the 3 of their papers we have counts for
3 papers
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti +1
This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called \textit{…
Deep Multi-class Adversarial Specularity Removal
John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti +2
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single imag…
Generative Collaborative Networks for Single Image Super-Resolution
Mohamed El Amine Seddik, Mohamed Tamaazousti, John Lin
A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large…