CyCADA: Cycle-Consistent Adversarial Domain Adaptation

ICML 2018 Judy HoffmanEric TzengTaesung ParkJun-Yan ZhuPhillip IsolaKate SaenkoAlexei A. EfrosTrevor Darrell

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... (read more)

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Task Dataset Model Metric name Metric value Global rank Compare
Synthetic-to-Real Translation GTAV-to-Cityscapes Labels CyCADA pixel+feat mIoU 39.5 # 3
Synthetic-to-Real Translation GTAV-to-Cityscapes Labels CyCADA pixel+feat fwIOU 72.4 # 1
Synthetic-to-Real Translation GTAV-to-Cityscapes Labels CyCADA pixel+feat Per-pixel Accuracy 82.3% # 1
Unsupervised Image-To-Image Translation SVNH-to-MNIST CyCADA pixel+feat Classification Accuracy 90.4% # 1
Image-to-Image Translation SYNTHIA Fall-to-Winter CyCADA mIoU 63.3 # 1
Image-to-Image Translation SYNTHIA Fall-to-Winter CyCADA Per-pixel Accuracy 92.1% # 1
Image-to-Image Translation SYNTHIA Fall-to-Winter CyCADA fwIOU 85.7 # 1