Alexandre Araujo | DeepAI

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∙ 07/27/2023

R-LPIPS: An Adversarially Robust Perceptual Similarity Metric

Similarity metrics have played a significant role in computer vision to ...

0 Sara Ghazanfari, et al. ∙

∙ 06/16/2023

Towards Better Certified Segmentation via Diffusion Models

The robustness of image segmentation has been an important research topi...

6 Othmane Laousy, et al. ∙

∙ 05/25/2023

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

Neural networks are known to be susceptible to adversarial samples: smal...

0 Haotian Xue, et al. ∙

∙ 05/25/2023

Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration

Since the control of the Lipschitz constant has a great impact on the tr...

0 Blaise Delattre, et al. ∙

∙ 03/06/2023

A Unified Algebraic Perspective on Lipschitz Neural Networks

Important research efforts have focused on the design and training of ne...

0 Alexandre Araujo, et al. ∙

∙ 06/03/2022

Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis

Randomized smoothing is the dominant standard for provable defenses agai...

0 Raphael Ettedgui, et al. ∙

∙ 10/25/2021

Scalable Lipschitz Residual Networks with Convex Potential Flows

The Lipschitz constant of neural networks has been established as a key ...

0 Laurent Meunier, et al. ∙

∙ 09/02/2021

Building Compact and Robust Deep Neural Networks with Toeplitz Matrices

Deep neural networks are state-of-the-art in a wide variety of tasks, ho...

0 Alexandre Araujo, et al. ∙

∙ 12/04/2020

Advocating for Multiple Defense Strategies against Adversarial Examples

It has been empirically observed that defense mechanisms designed to pro...

0 Alexandre Araujo, et al. ∙

∙ 06/15/2020

Fast Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization

This paper tackles the problem of Lipschitz regularization of Convolutio...

0 Alexandre Araujo, et al. ∙

∙ 03/25/2019

Robust Neural Networks using Randomized Adversarial Training

Since the discovery of adversarial examples in machine learning, researc...

0 Alexandre Araujo, et al. ∙

∙ 02/04/2019

Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family

This paper investigates the theory of robustness against adversarial att...

0 Rafael Pinot, et al. ∙

∙ 01/29/2019

On the Expressive Power of Deep Fully Circulant Neural Networks

In this paper, we study deep fully circulant neural networks, that is de...

0 Alexandre Araujo, et al. ∙

∙ 10/02/2018

Training compact deep learning models for video classification using circulant matrices

In real world scenarios, model accuracy is hardly the only factor to con...

0 Alexandre Araujo, et al. ∙

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