  Deep neural networks have been widely studied in autonomous driving
applications such as semantic segmentation or depth estimation. However,
training a neural network in a supervised manner requires a large amount of
annotated labels which are expensive and time-consuming to collect. Recent
studies leverage synthetic data collected from a virtual environment which are
much easier to acquire and more accurate compared to data from the real world,
but they usually suffer from poor generalization due to the inherent domain
shift problem. In this paper, we propose a Domain-Agnostic Contrastive Learning
(DACL) which is a two-stage unsupervised domain adaptation framework with
cyclic adversarial training and contrastive loss. DACL leads the neural network
to learn domain-agnostic representation to overcome performance degradation
when there exists a difference between training and test data distribution. Our
proposed approach achieves better performance in the monocular depth estimation
task compared to previous state-of-the-art methods and also shows effectiveness
in the semantic segmentation task.
