Portrait of Gaétan Marceau Caron

Gaétan Marceau Caron

gaetan.marceau.caron@mila.quebec
Senior Director, Applied Machine Learning Research

Biography

Gaétan Marceau Caron is the Senior Director of the Applied Machine Learning Research team at Mila – Quebec Artificial Intelligence Research Institute. He works to promote Mila’s team of researchers who work jointly with industry to address difficult scientific problems using AI. The spin-offs of these projects will ultimately benefit all of Canadian society.

He has more than twelve years’ experience in knowledge transfer in the field of AI through working on numerous collaborative projects in applied research. He has dual expertise in engineering and scientific research, having completed engineering degrees at Polytechnique Montréal and the Institut Polytechnique de Paris (ENSTA Paris), and science degrees at Université Pierre-et-Marie-Curie (Sorbonne) and Université Paris-Saclay at the PhD level. This enables him to analyze industrial systems and find innovative solutions to the increasingly complex challenges facing society.

In his nine years at Mila, he has served as scientific adviser on more than twenty-five projects with industry and taught in six editions of the annual Mila/IVADO Deep Learning School.

Publications

OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection
Victor Livernoche
Akshatha Arodi
Andreea Musulan
Zachary Yang
Adam Salvail
Ga'etan Marceau Caron
Jean-François Godbout
Reihaneh Rabbany
Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically… (see more) sensitive contexts. This challenge is amplified by the increasing realism of modern generative models, which our human perception study confirms are often indistinguishable from real images. Yet, existing deepfake detection benchmarks rely on outdated generators or narrowly scoped datasets (e.g., single-face imagery), limiting their utility for real-world detection. To address these gaps, we present OpenFake, a large politically grounded dataset specifically crafted for benchmarking against modern generative models with high realism, and designed to remain extensible through an innovative crowdsourced adversarial platform that continually integrates new hard examples. OpenFake comprises nearly four million total images: three million real images paired with descriptive captions and almost one million synthetic counterparts from state-of-the-art proprietary and open-source models. Detectors trained on OpenFake achieve near-perfect in-distribution performance, strong generalization to unseen generators, and high accuracy on a curated in-the-wild social media test set, significantly outperforming models trained on existing datasets. Overall, we demonstrate that with high-quality and continually updated benchmarks, automatic deepfake detection is both feasible and effective in real-world settings.
2025-09-10
ArXiv (preprint)
doi.org
arxiv.org
CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset
Akshatha Arodi
Margaux Luck
Jean-Luc Bedwani
Aldo Zaimi
Ge Li
Nicolas Pouliot
Julien Beaudry
Gaétan Marceau Caron
Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to spec… (see more)ific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (VAD) for robotic power line inspection. While existing VAD methods perform well in controlled environments, real-world scenarios present diverse and unexpected anomalies that current datasets fail to capture. To address this gap, we introduce CableInspect-AD, a high-quality, publicly available dataset created and annotated by domain experts from Hydro-Québec, a Canadian public utility. This dataset includes high-resolution images with challenging real-world anomalies, covering defects with varying severity levels. To address the challenges of collecting diverse anomalous and nominal examples for setting a detection threshold, we propose an enhancement to the celebrated PatchCore algorithm. This enhancement enables its use in scenarios with limited labeled data. We also present a comprehensive evaluation protocol based on cross-validation to assess models' performances. We evaluate our Enhanced-PatchCore for few-shot and many-shot detection, and Vision-Language Models for zero-shot detection. While promising, these models struggle to detect all anomalies, highlighting the dataset's value as a challenging benchmark for the broader research community. Project page: https://mila-iqia.github.io/cableinspect-ad/.
2024-12-10
Neural Information Processing Systems (Accept (Poster))
doi.org
arxiv.org
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio
Prateek Gupta
Tegan Maharaj
Nasim Rahaman
Martin Weiss
Tristan Deleu
Eilif Muller
Meng Qu
Victor Schmidt
Pierre-Luc St-Charles
Hannah Alsdurf
Olexa Bilanuik
David Buckeridge
Gaétan Marceau Caron
Pierre-Luc Carrier
Joumana Ghosn
Satya Ortiz-Gagné
Chris Pal
Irina Rish
Bernhard Schölkopf … (see 3 more)
Abhinav Sharma
Jian Tang
Andrew Williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
2021-05-02
International Conference on Learning Representations (Spotlight)
doi.org
openreview.net
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Abhinav Sharma
Nanor Minoyan
Soren Harnois Leblanc
Victor Schmidt
Pierre-Luc St. Charles
Tristan Deleu
Andrew Williams
Akshay Patel
Meng Qu
Olexa Bilaniuk
Gaétan Marceau Caron
Pierre Luc Carrier
Satya Ortiz-Gagné
Marc-Andre Rousseau
David Buckeridge … (see 9 more)
Joumana Ghosn
Yang Zhang
Bernhard Schölkopf
Jian Tang
Irina Rish
Christopher Pal
Joanna Merckx
Eilif B. Muller
Yoshua Bengio
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and vari… (see more)ous digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.
2020-10-01
OpenReview.net/Anonymous_Preprint (unknown)
doi.org
openreview.net