Search Results for author:
Found 3 papers, 1 papers with code
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
We compare PhysGAN with a set of state-of-the-art baseline methods including several of our self-designed ones, which further demonstrate the robustness and efficacy of our approach.
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
In this paper, we address this key challenge by proposing a semi-supervised multi-task learning framework called \sysname{} which aims to intrinsically search for a latent space suitable for detecting labels of instances from both known and unknown classes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
