Maya Kabkab — accepted AI-conference papers

2022

R4D: Utilizing Reference Objects for Long-Range Distance Estimation

Yingwei Li, Tiffany Chen, Maya Kabkab, Ruichi Yu, Longlong Jing, Yurong You +1

ICLR 2022poster

Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important long-range objects. In this paper, we introduce a challenging and under-explored task, which we refer to as Long-Range Dis…

Cited by 7 Source ↗PDF ↗Scholar ↗

2018

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Pouya Samangouei, Maya Kabkab, Rama Chellappa

ICLR 2018poster

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose De…

Cited by 1543 Source ↗PDF ↗code ↗Scholar ↗