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Thibaut Boissin

9 accepted papers

2025

An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures

Thibaut Boissin, Franck Mamalet, Thomas Fel, Agustin Martin Picard, Thomas Massena, Mathieu Serrurier
ICML 2025poster

Orthogonal convolutional layers are valuable components in multiple areas of machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitz-constrained models. Their ability to preserve norms and ensure stable gradient propagation makes them valuable for a large range of pr…

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2025

Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks

Thomas Massena, Léo Andéol, Thibaut Boissin, Franck Mamalet, Corentin Friedrich, Mathieu Serrurier +1
ICML 2025poster

Conformal Prediction (CP) has proven to be an effective post-hoc method for improving the trustworthiness of neural networks by providing prediction sets with finite-sample guarantees. However, under adversarial attacks, classical conformal guarantees do not hold anymore: this problem is addressed i…

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2024

DP-SGD Without Clipping: The Lipschitz Neural Network Way

Louis Béthune, Thomas Massena, Thibaut Boissin, Aurélien Bellet, Franck Mamalet, Yannick Prudent +3
ICLR 2024poster

State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face difficulties to estimate tight bounds on the sensitivity of the network's layers, and instead rely on a process of per-sample gradient clipping. This clipping process not only biases the direction of…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2023

CRAFT: Concept Recursive Activation FacTorization for Explainability

Thomas Fel, Agustin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin +2
CVPR 2023poster

Attribution methods are a popular class of explainability methods that use heatmaps to depict the most important areas of an image that drive a model decision. Nevertheless, recent work has shown that these methods have limited utility in practice, presumably because they only highlight the most sal…

Cited by 124Source ↗PDF ↗code ↗Scholar ↗
2023

On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective

Mathieu Serrurier, Franck Mamalet, Thomas FEL, Louis Béthune, Thibaut Boissin
NeurIPS 2023poster

Input gradients have a pivotal role in a variety of applications, including adversarial attack algorithms for evaluating model robustness, explainable AI techniques for generating saliency maps, and counterfactual explanations. However, saliency maps generated by traditional neural networks are ofte…

Cited by 10Source ↗PDF ↗Scholar ↗
2023

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin VINCENOT +1
ICML 2023poster

We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. The distance to the support can be interpreted as a normality score, and i…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2023

Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization

Thomas FEL, Thibaut Boissin, Victor Boutin, Agustin Martin Picard, Paul Novello, Julien Colin +6
NeurIPS 2023poster

Feature visualization has gained significant popularity as an explainability method, particularly after the influential work by Olah et al. in 2017. Despite its success, its widespread adoption has been limited due to issues in scaling to deeper neural networks and the reliance on tricks to generate…

Cited by 19Source ↗PDF ↗Scholar ↗
2022

Pay attention to your loss : understanding misconceptions about Lipschitz neural networks

Louis Béthune, Thibaut Boissin, Mathieu Serrurier, Franck Mamalet, Corentin Friedrich, Alberto Gonzalez Sanz
NeurIPS 2022accept

Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning a…

Cited by 47Source ↗PDF ↗Scholar ↗
2021

Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization

Mathieu Serrurier, Franck Mamalet, Alberto Gonzalez-Sanz, Thibaut Boissin, Jean-Michel Loubes, Eustasio del Barrio
CVPR 2021poster

Adversarial examples have pointed out Deep Neural Network's vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, b…

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