Domain adaptation is critical for success in new, unseen environments.
 Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.
 Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.
 We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.
 CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.   Our model can be applied in a variety of visual recognition and prediction settings.
 We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.
An unsupervised domain adaptation approach which adapts at both the pixel and feature levels This paper proposes a domain adaptation approach by extending the CycleGAN with task specific loss functions and loss imposed over both pixels and features.  This paper proposes the use of CycleGANs for Domain Adaptation This paper makes a novel extension to the previous work on CycleGAN by coupling it with adversarial adaptation approaches, including a new feature and semantic loss in the overall objective of the CycleGAN, with clear benefits.