58 citations · 102 across the 5 of their papers we have counts for
5 papers
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
Marissa Dotter, Sherry Xie, Keith Manville +3
Machine Learning (ML) models are known to be vulnerable to adversarial inputs and researchers have demonstrated that even production systems, such as self-driving cars and ML-as-a-…
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)
Chris M. Ward, Josh Harguess, Brendan Crabb +1
Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing m…
Leveraging synthetic imagery for collision-at-sea avoidance
Chris M. Ward, Josh Harguess, Alexander G. Corelli
Maritime collisions involving multiple ships are considered rare, but in 2017 several United States Navy vessels were involved in fatal at-sea collisions that resulted in the death…
Ship classification from overhead imagery using synthetic data and domain adaptation
Chris M. Ward, Josh Harguess, Cameron Hilton
In this paper, we revisit the problem of classifying ships (maritime vessels) detected from overhead imagery. Despite the last decade of research on this very important and pertine…
Generative NeuroEvolution for Deep Learning
Phillip Verbancsics, Josh Harguess
An important goal for the machine learning (ML) community is to create approaches that can learn solutions with human-level capability. One domain where humans have held a signific…