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We tackle an unsupervised domain adaptation problem for which the domaindiscrepancy between labeled source and unlabeled target domains is large, dueto many factors of inter and intra-domain variation. While deep domainadaptation methods have been realized by reducing the domain discrepancy, theseare difficult to apply when domains are significantly unalike. In this work, wepropose to decompose domain discrepancy into multiple but smaller, and thuseasier to minimize, discrepancies by introducing unlabeled bridging domainsthat connect the source and target domains. We realize our proposal through anextension of the domain adversarial neural network with multiplediscriminators, each of which accounts for reducing discrepancies betweenunlabeled (bridge, target) domains and a mix of all precedent domains includingsource. We validate the effectiveness of our method on several adaptation tasksincluding object recognition and semantic segmentation.

Adaptation Across Extreme Variations using Unlabeled Domain Bridges