Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
This work addresses the application of adversarial techniques for autonomous driving, but it is incremental as it primarily reviews and formalizes existing methods rather than introducing new ones.
The paper tackles the problem of applying Generative Adversarial Networks (GANs) to autonomous driving, focusing on areas like data augmentation and loss function learning, and reviews key applications and challenges in this domain.
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.