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.