activity
20182023
most citedTheoretical evidence for adversarial robustness through randomization

35 citations · 90 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CV2023

Towards Real-World Focus Stacking with Deep Learning

Alexandre Araujo, Jean Ponce, Julien Mairal

Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shall…

eess.IV2023

Certification of Deep Learning Models for Medical Image Segmentation

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +3

In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification mode…

cs.LG2023

The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing

Blaise Delattre, Alexandre Araujo, Quentin Barthélemy +1

Real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. The certified radius in this context…

cs.CV2023★ 12 cited

R-LPIPS: An Adversarially Robust Perceptual Similarity Metric

Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy +2

Similarity metrics have played a significant role in computer vision to capture the underlying semantics of images. In recent years, advanced similarity metrics, such as the Learne…

cs.LG2023

Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration

Blaise Delattre, Quentin Barthélemy, Alexandre Araujo +1

Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowaday…

cs.CV2023★ 2 cited

Towards Better Certified Segmentation via Diffusion Models

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +4

The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classif…