Search Results for author: Adel Bibi

Found 22 papers, 10 papers with code

ANCER: Anisotropic Certification via Sample-wise Volume Maximization

1 code implementation9 Jul 2021 Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi

All prior art on randomized smoothing has focused on isotropic $\ell_p$ certification, which has the advantage of yielding certificates that can be easily compared among isotropic methods via $\ell_p$-norm radius.

DeformRS: Certifying Input Deformations with Randomized Smoothing

1 code implementation2 Jul 2021 Motasem Alfarra, Adel Bibi, Naeemullah Khan, Philip H. S. Torr, Bernard Ghanem

Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e. g. translations, rotations, etc.

On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective

no code implementations1 Jan 2021 Motasem Alfarra, Adel Bibi, Hasan Abed Al Kader Hammoud, Mohamed Gaafar, Bernard Ghanem

This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations.

Network Pruning

Data Dependent Randomized Smoothing

1 code implementation8 Dec 2020 Motasem Alfarra, Adel Bibi, Philip H. S. Torr, Bernard Ghanem

In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smooth classifier.

Network Moments: Extensions and Sparse-Smooth Attacks

no code implementations21 Jun 2020 Modar Alfadly, Adel Bibi, Emilio Botero, Salman AlSubaihi, Bernard Ghanem

This has incited research on the reaction of DNNs to noisy input, namely developing adversarial input attacks and strategies that lead to robust DNNs to these attacks.

Rethinking Clustering for Robustness

1 code implementation13 Jun 2020 Motasem Alfarra, Juan C. Pérez, Adel Bibi, Ali Thabet, Pablo Arbeláez, Bernard Ghanem

This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness.

On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective

no code implementations20 Feb 2020 Motasem Alfarra, Adel Bibi, Hasan Hammoud, Mohamed Gaafar, Bernard Ghanem

Our main finding is that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes.

Network Pruning

Analytical Moment Regularizer for Training Robust Networks

no code implementations ICLR 2020 Modar Alfadly, Adel Bibi, Muhammed Kocabas, Bernard Ghanem

In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.

Data Augmentation

Expected Tight Bounds for Robust Deep Neural Network Training

no code implementations25 Sep 2019 Salman AlSubaihi, Adel Bibi, Modar Alfadly, Abdullah Hamdi, Bernard Ghanem

al. that bounded input intervals can be inexpensively propagated from layer to layer through deep networks.

On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective

no code implementations25 Sep 2019 Motasem Alfarra, Adel Bibi, Hasan Hammoud, Mohamed Gaafar, Bernard Ghanem

We use tropical geometry, a new development in the area of algebraic geometry, to provide a characterization of the decision boundaries of a simple neural network of the form (Affine, ReLU, Affine).

Network Pruning

Constrained K-means with General Pairwise and Cardinality Constraints

1 code implementation24 Jul 2019 Adel Bibi, Baoyuan Wu, Bernard Ghanem

In this paper, we enforce the above two categories into a unified clustering model starting with the integer program formulation of the standard K-means.

Expected Tight Bounds for Robust Training

2 code implementations28 May 2019 Salman Al-Subaihi, Adel Bibi, Modar Alfadly, Abdullah Hamdi, Bernard Ghanem

In this paper, we closely examine the bounds of a block of layers composed in the form of Affine-ReLU-Affine.

Deep Layers as Stochastic Solvers

no code implementations ICLR 2019 Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl

In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex optimization objective with a single iteration of a $\tau$-nice Proximal Stochastic Gradient method.

Analytical Moment Regularizer for Gaussian Robust Networks

1 code implementation24 Apr 2019 Modar Alfadly, Adel Bibi, Bernard Ghanem

Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours.

Data Augmentation

Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input

no code implementations CVPR 2018 Adel Bibi, Modar Alfadly, Bernard Ghanem

Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks.

Image Classification

High Order Tensor Formulation for Convolutional Sparse Coding

no code implementations ICCV 2017 Adel Bibi, Bernard Ghanem

Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community.

Video Reconstruction

FFTLasso: Large-Scale LASSO in the Fourier Domain

no code implementations CVPR 2017 Adel Bibi, Hani Itani, Bernard Ghanem

Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e. g. on a GPU).

Dimensionality Reduction Face Recognition +2

3D Part-Based Sparse Tracker With Automatic Synchronization and Registration

no code implementations CVPR 2016 Adel Bibi, Tianzhu Zhang, Bernard Ghanem

In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D.

Occlusion Handling

In Defense of Sparse Tracking: Circulant Sparse Tracker

no code implementations CVPR 2016 Tianzhu Zhang, Adel Bibi, Bernard Ghanem

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework.

Visual Tracking

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