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Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. Wedoi:10.1109/cvpr.2018.00887 dblp:conf/cvpr/Sankaranarayanan18a fatcat:y3ueuaswxjbrdhbxgsz2kacbqu