1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2022
Causal Transportability for Visual Recognition
Chengzhi Mao, Kevin Xia, James Wang +4
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poo…
cs.CR2022★ 1 cited
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
Wei Hao, Aahil Awatramani, Jiayang Hu +5
Full-precision deep learning models are typically too large or costly to deploy on edge devices. To accommodate to the limited hardware resources, models are adapted to the edge us…
cs.CV2022
Using Multiple Self-Supervised Tasks Improves Model Robustness
Matthew Lawhon, Chengzhi Mao, Junfeng Yang
Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a n…