5 citations · 12 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…