Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.
@article{arxiv.1902.03442,
title = {Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving},
author = {Michal Uricar and Pavel Krizek and David Hurych and Ibrahim Sobh and Senthil Yogamani and Patrick Denny},
journal= {arXiv preprint arXiv:1902.03442},
year = {2020}
}
Comments
Accepted for publication in Electronic Imaging, Autonomous Vehicles and Machines 2019. arXiv admin note: text overlap with arXiv:1606.05908 by other authors