Adaptation Across Extreme Variations using Unlabeled Bridges

Adaptation Across Extreme Variations using Unlabeled BridgesPublication Date: 9/7/2020

Event: BMVC’20, Manchester, UK

Reference: pp. 1-13, 2020

Authors: Shuyang Dai, Duke University, NEC Laboratories America, Inc.; Kihyuk Sohn, NEC Laboratories America, Inc.; Yi-Hsuan Tsai, NEC Laboratories America, Inc.; Lawrence Carin, Duke University; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter- and intra-domain variation. While deep domain adaptation methods have been realized by reducing the domain discrepancy, these are difficult to apply when domains are significantly different. We propose to decompose domain discrepancy into multiple but smaller, and thus easier to minimize, discrepancies by introducing unlabeled bridging domains that connect the source and target domains. We realize our proposed approach through an extension of the domain adversarial neural network with multiple discriminators, each of which accounts for reducing discrepancies between unlabeled (bridge, target) domains and a mix of all precedent domains including source. We validate the effectiveness of our method on several adaptation tasks including object recognition and semantic segmentation.

Publication Link:

Supplemental Publication Link: