Christian Gagné

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

Frédéric Beaupré

PhD - Université Laval

Catherine Bouchard

PhD - Université Laval

Olivier Bussière

Master's Research - Université Laval

olivier2202@gmail.com

Cynthia García Ybarra

Master's Research - Université Laval

Sara Karami

PhD - Université Laval

Benjamin Léger

PhD - Université Laval

Jonas Ngnawe

PhD - Université Laval

Fatemeh Nourilenjan Nokabadi

PhD - Université Laval

PhD - Université Laval

Adam Tupper

PhD - Université Laval

adam.tupper.1@ulaval.ca

Publications

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

2023-11-26

ArXiv (preprint)

Towards More General Loss and Setting in Unsupervised Domain Adaptation

Changjian Shui

Ruizhi Pu

Gezheng Xu

Jun Wen

Fan Zhou

Charles Ling

Boyu Wang

In this article, we present an analysis of unsupervised domain adaptation with a series of theoretical and algorithmic results. We derive a … (see more)novel Rényi-

2023-10-01

IEEE Transactions on Knowledge and Data Engineering (published)

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Catherine Bouchard

Theresa Wiesner

Andréanne Deschênes

Anthony Bilodeau

Benoit Turcotte

Flavie Lavoie-Cardinal

2023-07-27

Nature Machine Intelligence (published)

Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning

Mohamed Abderrahmen Abid

Arman Afrasiyabi

Ihsen Hedhli

Jean‐François Lalonde

2023-05-08

ArXiv (preprint)

Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

Thomas Philippon

Neural network ensembles have been studied extensively in the context of adversarial robustness and most ensemble-based approaches remain vu… (see more)lnerable to adaptive attacks. In this paper, we investigate the robustness of Error-Correcting Output Codes (ECOC) ensembles through architectural improvements and ensemble diversity promotion. We perform a comprehensive robustness assessment against adaptive attacks and investigate the relationship between ensemble diversity and robustness. Our results demonstrate the benefits of ECOC ensembles for adversarial robustness compared to regular ensembles of convolutional neural networks (CNNs) and show why the robustness of previous implementations is limited. We also propose an adversarial training method specific to ECOC ensembles that allows to further improve robustness to adaptive attacks.

2023-03-04

ArXiv (preprint)

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Jean-François Pelletier

Louis Rochette

Eric Pelletier

Pascale Lévesque

Victoria Massamba

Camille Brousseau-Paradis

Mada Mohammed

Geneviève Gariépy

Alain Lesage

2023-02-27

BMJ Open (published)

Filtering Pixel Latent Variables for Unmixing Volumetric Images

Catherine Bouchard

Vincent Boulanger

Flavie Lavoie-Cardinal

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)

Filtering Pixel Latent Variables for Unmixing Volumetric Images

Catherine Bouchard

Vincent Boulanger

Flavie Lavoie-Cardinal

2023-01-01

arXiv.org (preprint)

Gap Minimization for Knowledge Sharing and Transfer

Boyu Wang

Jorge A. Mendez

Changjian Shui

Fan Zhou

Di Wu

Gezheng Xu

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.,

Lifelong Online Learning from Accumulated Knowledge

Changjian Shui

William Wang

Ihsen Hedhli

Chi Man Wong

Feng Wan

Boyu Wang

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)

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

Morgane Lemasson

Geneviève Gariépy

Chantal Mérette

Elham Rahme

Alain Lesage

2022-09-26

Social Psychiatry and Psychiatric Epidemiology (published)

Matching Feature Sets for Few-Shot Image Classification

Arman Afrasiyabi

Hugo Larochelle

Jean‐François Lalonde

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)