121 citations · 505 across the 31 of their papers we have counts for
4 papers · 1 filter
Adaptive Quantization for Deep Neural Network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung +1
In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNN…
Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance propert…
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…