7 citations · 30 across the 16 of their papers we have counts for
19 papers
Online Distillation with Continual Learning for Cyclic Domain Shifts
Joachim Houyon, Anthony Cioppa, Yasir Ghunaim +5
In recent years, online distillation has emerged as a powerful technique for adapting real-time deep neural networks on the fly using a slow, but accurate teacher model. However, a…
Evaluation of Test-Time Adaptation Under Computational Time Constraints
Motasem Alfarra, Hani Itani, Alejandro Pardo +6
This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. T…
Real-Time Evaluation in Online Continual Learning: A New Hope
Yasir Ghunaim, Adel Bibi, Kumail Alhamoud +5
Current evaluations of Continual Learning (CL) methods typically assume that there is no constraint on training time and computation. This is an unrealistic assumption for any real…
PIVOT: Prompting for Video Continual Learning
Andrés Villa, Juan León Alcázar, Motasem Alfarra +5
Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult…
SimCS: Simulation for Domain Incremental Online Continual Segmentation
Motasem Alfarra, Zhipeng Cai, Adel Bibi +2
Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual le…
Generalizability of Adversarial Robustness Under Distribution Shifts
Kumail Alhamoud, Hasan Abed Al Kader Hammoud, Motasem Alfarra +1
Recent progress in empirical and certified robustness promises to deliver reliable and deployable Deep Neural Networks (DNNs). Despite that success, most existing evaluations of DN…
Certified Robustness in Federated Learning
Motasem Alfarra, Juan C. Pérez, Egor Shulgin +2
Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as…
3DeformRS: Certifying Spatial Deformations on Point Clouds
Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra +2
3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these model…
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
Juan C. Pérez, Motasem Alfarra, Ali Thabet +2
Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face Recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose…
On the Robustness of Quality Measures for GANs
Motasem Alfarra, Juan C. Pérez, Anna Frühstück +3
This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fréchet Inception Distance (FID). Analogous to the vulnerability of dee…
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformatio…
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Francisco Eiras, Motasem Alfarra, M. Pawan Kumar +4
Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale. All prior art on randomized smoothing has foc…
DeformRS: Certifying Input Deformations with Randomized Smoothing
Motasem Alfarra, Adel Bibi, Naeemullah Khan +2
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, r…
Combating Adversaries with Anti-Adversaries
Motasem Alfarra, Juan C. Pérez, Ali Thabet +3
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing…
Data-Dependent Randomized Smoothing
Motasem Alfarra, Adel Bibi, Philip H. S. Torr +1
Randomized smoothing is a recent technique that achieves state-of-art performance in training certifiably robust deep neural networks. While the smoothing family of distributions i…
Rethinking Clustering for Robustness
Motasem Alfarra, Juan C. Pérez, Adel Bibi +3
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness. Recent works observed that Adversarial Trainin…
Adaptive Learning of the Optimal Batch Size of SGD
Motasem Alfarra, Slavomir Hanzely, Alyazeed Albasyoni +2
Recent advances in the theoretical understanding of SGD led to a formula for the optimal batch size minimizing the number of effective data passes, i.e., the number of iterations t…
On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective
Motasem Alfarra, Adel Bibi, Hasan Hammoud +2
This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations. We use tropical geomet…
Gabor Layers Enhance Network Robustness
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect on robustness against adversarial attacks of replacing…