Orateurs
Description
Title: Explainable AI (XAI) and visual recognition models
Abstract: First, a one hour presentation will provide details on the various types of explanations methods, pros and cons. XAI include two main types of methods: post-hoc or passive and transparency or active. Posthoc methods aims at explaining an already trained mode, while active methods aims at rendering the model more transparent or explainable by design. Another set of method focus on Concepts that can be active or passive and that provide more understandable features in their explanations.
Then a short presentation on some works held at IRIT, such as Fusion-CAM, and a demo on evaluating the discriminativity of Class Activation Maps (CAMs) across CNN architectures, layers, and target classes in multi-label classification, and their faithfulness to the model’s decision-making process.
Finally a 1:30 practical session will allow you to delve into some standard explanations such as gradCAM.
Bio:
Moncef Garouani is an Associate Professor of Computer Science at Université Toulouse Capitole and a researcher at IRIT. His research interests lie at the intersection of Machine Learning, Explainable Artificial Intelligence (XAI), multimodal learning, and Responsible AI. His work focuses on developing transparent, trustworthy, and human-centered AI systems, with particular emphasis on explainability, automated machine learning, multimodal data analysis, and the responsible design, governance, and deployment of AI.
Ronan Sicre is junior professor at University of Toulouse and IRIT since 2025, after being assistant professor for 8 years at Ecole Centrale Méditerranée and LIS. He add prior postdoc positions at INRIA Rennes, University of Caen, University of Amsterdam and obtain his PhD from the University of Bordeaux. He works in the field of deep learning, computer vision and explainable AI.
Hajar Dekdegue, PhD student at IRIT, Université de Toulouse.