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The effectiveness of GANs in producing images according to a specific visual domain has shown potential in unsupervised domain adaptation. Source labeled images have been modified to mimic target samples for training classifiers in the target domain, and inverse mappings from the target to the source domain have also been evaluated, without new image generation. In this paper we aim at getting the best of both worlds by introducing a symmetric mapping among domains. We jointly optimizedoi:10.1109/cvpr.2018.00845 dblp:conf/cvpr/RussoCTC18 fatcat:5mzmtmbq6fd5tpvx4fe6vcbvqm