58 citations · 130 across the 6 of their papers we have counts for
11 papers
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…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Generalized Real-World Super-Resolution through Adversarial Robustness
Angela Castillo, María Escobar, Juan C. Pérez +4
Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution i…
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…
Towards Robust General Medical Image Segmentation
Laura Daza, Juan C. Pérez, Pablo Arbeláez
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses ha…
SIMBA: Specific Identity Markers for Bone Age Assessment
Cristina González, María Escobar, Laura Daza +3
Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity marker…