7 citations · 19 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…