30 citations · 42 across the 12 of their papers we have counts for
3 papers · 1 filter
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Nathan Drenkow, Numair Sani, Ilya Shpitser +1
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-impactful applications, motivating the need to close the gap in model performance…
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
Zhaoshuo Li, Nathan Drenkow, Hao Ding +5
Scene depth estimation from stereo and monocular imagery is critical for extracting 3D information for downstream tasks such as scene understanding. Recently, learning-based method…
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…