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Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation
Specifically, we propose a novel alignment loss term that minimizes the kernel Bures-Wasserstein distance between the joint distributions.
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
Second, the batch-wise training manner in deep learning limits the description of the global structure.
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