Portrait de Christian Gagné

Christian Gagné

Membre académique associé
Chaire en IA Canada-CIFAR
Professeur titulaire, Université Laval, Département de génie électrique et informatique
Directeur, Institute Intelligence and Data (IID)
Sujets de recherche
Apprentissage automatique médical
Apprentissage de la programmation
Apprentissage de représentations
Apprentissage profond
Vision par ordinateur
Site web
Google Scholar
LinkedIn
X

Biographie

Christian Gagné est professeur au Département de génie électrique et de génie informatique de l’Université Laval depuis 2008, et dirige l’Institut intelligence et données (IID). Il détient une chaire en IA Canada-CIFAR et est membre associé à Mila – Institut québécois d’intelligence artificielle. Il est également membre du Laboratoire de vision et systèmes numériques (LVSN), une composante du Centre de recherche en robotique, vision et intelligence machine (CeRVIM) ainsi que du Centre de recherche en données massives (CRDM) de l’Université Laval. Il fait partie des regroupements stratégiques REPARTI et UNIQUE du Fonds de recherche du Québec – Nature et technologies (FRQNT), du centre VITAM du Fonds de recherche du Québec – Santé (FRQS) et de l’Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA).

Ses intérêts de recherche portent sur l’élaboration de méthodes pour l’apprentissage automatique et l’optimisation stochastique. En particulier, il se consacre aux réseaux de neurones profonds, à l’apprentissage et au transfert de représentations, au méta-apprentissage ainsi qu’à l’apprentissage multitâche. Il s’intéresse également aux approches d’optimisation basées sur des modèles probabilistes ainsi qu’aux algorithmes évolutionnaires, entre autres pour l’optimisation boîte noire et la programmation automatique. Une part importante de ses travaux porte également sur la mise en pratique de ces techniques dans des domaines comme la vision numérique, la microscopie, la santé, l’énergie et les transports.

Étudiants actuels

Frédéric Beaupré
Doctorat - Université Laval
Github
Catherine Bouchard
Doctorat - Université Laval
Github
Google Scholar
Cynthia García Ybarra
Maîtrise recherche - Université Laval
Sara Karami
Doctorat - Université Laval
Benjamin Léger
Doctorat - Université Laval
Jonas Ngnawe
Doctorat - Université Laval
Site web
Github
Google Scholar
Fatemeh Nourilenjan Nokabadi
Doctorat - Université Laval
Site web
Github
Google Scholar
Sabyasachi Sahoo
Doctorat - Université Laval
Site web
Github
Google Scholar
Adam Tupper
Doctorat - Université Laval
adam.tupper.1@ulaval.ca
Site web
Github
Google Scholar

Publications

Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Hugo Larochelle
Jean‐François Lalonde
Christian Gagné
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (voir plus)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org
TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING
Simon Duchesne
Olivier Potvin
Daniel Gourdeau
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Diego Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Yang
Nathalie Duchesne
2022-04-04
Scientific Reports (publié)
doi.org
Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning
Daniel Gourdeau
Olivier Potvin
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Diego Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Yang
Nathalie Duchesne
Simon Duchesne
2022-04-04
Scientific Reports (publié)
doi.org
Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning
Daniel Gourdeau
Olivier Potvin
Patrick Archambault
Carl Chartrand‐lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Khiem Le
Simon Lemieux
Marie‐hélène Lévesque
Diego R. Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Y Yang
N. Duchesne
Simon Duchesne
2022-04-04
Scientific Reports (published)
doi.org
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi
Hugo Larochelle
Jean‐François Lalonde
Christian Gagné
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (voir plus)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.
2022-04-02
ArXiv (preprint)
doi.org
arxiv.org
Active Learning for Capturing Human Decision Policies in a Data Frugal Context
Loïc Grossetête
Alexandre Marois
Bénédicte Chatelais
Christian Gagné
Daniel Lafond
2022-02-02
Machine Learning, Optimization, and Data Science (publié)
doi.org
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui
Gezheng Xu
Qi CHEN
Jiaqi Li
Charles Ling
Tal Arbel
Boyu Wang
Christian Gagné
We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (voir plus)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.
openreview.net
On the benefits of representation regularization in invariance based domain generalization
Changjian Shui
Boyu Wang
Christian Gagné
2022-01-01
Machine Learning (publié)
doi.org
arxiv.org
On the benefits of representation regularization in invariance based domain generalization
Changjian Shui
Boyu Wang
Christian Gagné
2021-05-30
ArXiv (preprint)
doi.org
arxiv.org
Active Learning for Capturing Human Decision Policies in a Data Frugal Context
Loïc Grossetête
Alexandre Marois
Bénédicte Chatelais
Christian Gagné
Daniel Lafond
2021-01-01
International Conference on Machine Learning, Optimization, and Data Science (published)
doi.org
Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons
Flavie Lavoie-Cardinal
Anthony Bilodeau
Mado Lemieux
Marc-André Gardner
Theresa Wiesner
Gabrielle Laramée
Christian Gagné
Paul De Koninck
2020-07-20
Scientific Reports (publié)
doi.org
Deep Active Learning: Unified and Principled Method for Query and Training
Changjian Shui
Fan Zhou
Christian Gagné
Boyu Wang
2020-06-03
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
arxiv.org