190 citations · 950 across the 19 of their papers we have counts for
7 papers · 1 filter
SaaS: Speed as a Supervisor for Semi-supervised Learning
Safa Cicek, Alhussein Fawzi, Stefano Soatto
We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an ite…
Classification regions of deep neural networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard +1
The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions cr…
Robustness of classifiers to universal perturbations: a geometric perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +2
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclas…
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +1
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be m…
Manitest: Are classifiers really invariant?
Alhussein Fawzi, Pascal Frossard
Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-o…
Dictionary learning for fast classification based on soft-thresholding
Alhussein Fawzi, Mike Davies, Pascal Frossard
Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of com…
Image registration with sparse approximations in parametric dictionaries
Alhussein Fawzi, Pascal Frossard
We examine in this paper the problem of image registration from the new perspective where images are given by sparse approximations in parametric dictionaries of geometric function…