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Unsupervised Deep Domain Adaptation Based on Weighted Adversarial Network
Recent studies indicate that adversarial learning can reduce distribution discrepancy between domains effectively, but when the samples belonged to different classes have similar characteristics in the domains, they may be incorrectly aligned to similar classes after domain adaption, which gives rise to negative transfer. To prevent such misalignment, we propose a weighted adversarial network based unsupervised domain adaptation method. Its contributions are mainly reflected in the followingdoi:10.1109/access.2020.2984777 fatcat:oohi5jaalrg2lozs6zmnpzhccm