71 citations · 407 across the 58 of their papers we have counts for
6 papers · 1 filter
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning
Ahmadreza Jeddi, Mohammad Javad Shafiee, Alexander Wong
Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for improving the robustness of the deep neural networks. However, PGD AT has been shown to…
Self-Gradient Networks
Hossein Aboutalebi, Mohammad Javad Shafiee Alexander Wong
The incredible effectiveness of adversarial attacks on fooling deep neural networks poses a tremendous hurdle in the widespread adoption of deep learning in safety and security-cri…
AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers
Alexander Wong, Mahmoud Famouri, Mohammad Javad Shafiee
While significant advances in deep learning has resulted in state-of-the-art performance across a large number of complex visual perception tasks, the widespread deployment of deep…
Vulnerability Under Adversarial Machine Learning: Bias or Variance?
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One inte…
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi +2
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg +2
While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widesprea…