no code implementations • 6 Nov 2023 • Boris Joukovsky, Brent De Weerdt, Nikos Deligiannis
We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information.
1 code implementation • 20 Jun 2022 • Fawaz Sammani, Boris Joukovsky, Nikos Deligiannis
Contrastive learning has revolutionized the field of computer vision, learning rich representations from unlabeled data, which generalize well to diverse vision tasks.
no code implementations • 23 Nov 2020 • Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky, Maryna Kvasnytsia, Ine Dirks, Mitchel Alioscha-Perez, Nikos Deligiannis, Panagiotis Gonidakis, Sebastián Amador Sánchez, Redona Brahimetaj, Evgenia Papavasileiou, Jonathan Cheung-Wai Chana, Fei Li, Shangzhen Song, Yixin Yang, Sofie Tilborghs, Siri Willems, Tom Eelbode, Jeroen Bertels, Dirk Vandermeulen, Frederik Maes, Paul Suetens, Lucas Fidon, Tom Vercauteren, David Robben, Arne Brys, Dirk Smeets, Bart Ilsen, Nico Buls, Nina Watté, Johan de Mey, Annemiek Snoeckx, Paul M. Parizel, Julien Guiot, Louis Deprez, Paul Meunier, Stefaan Gryspeerdt, Kristof De Smet, Bart Jansen, Jef Vandemeulebroucke
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
no code implementations • 2 Oct 2020 • Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar, Nikos Deligiannis
This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) with application to video foreground-background separation.
no code implementations • 30 Apr 2020 • Boris Joukovsky, Pengpeng Hu, Adrian Munteanu
In this Letter, the authors propose a deep learning based method to perform semantic segmentation of clothes from RGB-D images of people.
no code implementations • 18 Mar 2020 • Huynh Van Luong, Boris Joukovsky, Nikos Deligiannis
In this line of research, this paper develops a novel deep recurrent neural network (coined reweighted-RNN) by the unfolding of a reweighted $\ell_1$-$\ell_1$ minimization algorithm and applies it to the task of sequential signal reconstruction.