3 papers
cs.CV2026
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Vincent Lébé, Yannick Prudent, Corentin Friedrich +3
Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their a…
stat.ML2024
Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods
Paul Novello, Yannick Prudent, Joseba Dalmau +2
Since the seminal paper of Hendrycks et al. arXiv:1610.02136, Post-hoc deep Out-of-Distribution (OOD) detection has expanded rapidly. As a result, practitioners working on safety-c…
cs.LG2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way
Louis Bethune, Thomas Massena, Thibaut Boissin +6
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 layer…