26 citations · 92 across the 41 of their papers we have counts for
5 papers · 1 filter
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks
Nicholas Soucy, Salimeh Yasaei Sekeh
Semantic segmentation models classifying hyperspectral images (HSI) are vulnerable to adversarial examples. Traditional approaches to adversarial robustness focus on training or re…
Theoretical Understanding of the Information Flow on Continual Learning Performance
Josh Andle, Salimeh Yasaei Sekeh
Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data sequentially. CL performance evaluates the model's ability to continually learn…
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability
Madan Ravi Ganesh, Salimeh Yasaei Sekeh, Jason J. Corso
Raw deep neural network (DNN) performance is not enough; in real-world settings, computational load, training efficiency and adversarial security are just as or even more important…
The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics
Joshua Andle, Nicholas Soucy, Simon Socolow +1
Several datasets exist which contain annotated information of individuals' trajectories. Such datasets are vital for many real-world applications, including trajectory prediction a…
CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering
Nicholas Soucy, Salimeh Yasaei Sekeh
Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in re…