Portrait of Christian Gagné

Christian Gagné

Associate Academic Member
Canada CIFAR AI Chair
Full Professor, Université Laval, Department of Electrical and Computer Engineering
Director of IID (Institute Intelligence and Data), Institute Intelligence and Data (IID)
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Biography

Christian Gagné has been a professor in the Department of Electrical and Computer Engineering at Université Laval since 2008.

He is the director of the Institute Intelligence and Data (IID), holds a Canada CIFAR AI Chair, and is an associate member of Mila – Quebec Artificial Intelligence Institute.

Gagné is also a member of Université Laval’s Computer Vision and Systems Laboratory (LVSN), as well as its Robotics, Vision and Machine Intelligence Research Centre (CeRVIM) and its Big Data Research Centre (CRDM). He is a member of the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM centre of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technologies (OBVIA).

Gagné’s research focuses on the development of methods for machine learning and stochastic optimization. In particular, he is interested in deep neural networks, representation learning and transfer, meta-learning and multitasking. He is also interested in optimization approaches based on probabilistic models and evolutionary algorithms, including black-box optimization and automatic programming. An important part of his work is the practical application of these techniques in fields like computer vision, microscopy, healthcare, energy and transportation.

Current Students

Publications

Filtering Pixel Latent Variables for Unmixing Volumetric Images
Catherine Bouchard
Vincent Boulanger
Flavie Lavoie-Cardinal
Christian Gagné
Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (see more)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.
2023-01-01
arXiv.org (preprint)
doi.org
Gap Minimization for Knowledge Sharing and Transfer
Boyu Wang
Jorge A. Mendez
Changjian Shui
Fan Zhou
Di Wu
Gezheng Xu
Christian Gagné
Eric R. Eaton
Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order … (see more)to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g.,
arxiv.org
Lifelong Online Learning from Accumulated Knowledge
Changjian Shui
William Wang
Ihsen Hedhli
Chi Man Wong
Feng Wan
Boyu Wang
Christian Gagné
In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (see more) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.
2022-10-17
ACM Transactions on Knowledge Discovery from Data (published)
doi.org
The 5-year longitudinal diagnostic profile and health services utilization of patients treated with electroconvulsive therapy in Quebec: a population-based study
Simon Lafrenière
Fatemeh Gholi-Zadeh-Kharrat
Caroline Sirois
Victoria Massamba
Louis Rochette
Camille Brousseau-Paradis
Simon Patry
Christian Gagné
Morgane Lemasson
Geneviève Gariépy
Chantal Mérette
Elham Rahme
Alain Lesage
2022-09-26
Social Psychiatry and Psychiatric Epidemiology (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… (see more)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) (published)
doi.org
arxiv.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 (published)
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… (see more)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 (published)
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… (see more)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 (published)
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