Core Deep Learning

Deep learning being now a key component of AD systems, it is important to get a better understanding of its inner workings, in particular the link between the specifics of the learning optimization and the key properities (performance, regularity, robustness, generalization) of the trained models. Among other things, we investigate the impact of popular batch normalization on standard learning procedures and the ability to learn through unsupervised distillation.


Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis

Victor Letzelter, Mathieu Fontaine, Mickaël Chen, Patrick Pérez, Slim Essid, and Gaël Richard
Advances in Neural Information Processing Systems (NeurIPS), 2023

Spherical perspective on learning with normalization layers

Simon Roburin, Yann de Mont-Marin, Andrei Bursuc, Renaud Marlet, Patrick Pérez, Mathieu Aubry
Neurocomputing, 2022

QuEST: Quantized Embedding Space for Transferring Knowledge

Himalaya Jain, Spyros Gidaris, Nikos Komodakis, Patrick Pérez, and Matthieu Cord
European Conference on Computer Vision (ECCV), 2020