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Aligning and balancing the marginal and conditional feature distributions are two critical procedures for unsupervised domain adaptation (UDA) problems. However, existing methods usually consider the former while ignoring the latter. To improve the problems of instability and imbalance, we propose the Adaptative Joint Distribution Adaptation Network (AJDAN) by analyzing the multi-modal interactions between the two types of distributions and adding a self-learning network to simultaneouslydoi:10.1109/access.2021.3096877 fatcat:wjhrcndtgra3tnx3risvcrcz3m