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Attention Regularized Laplace Graph for Domain Adaptation
In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely Attention Regularized Laplace Graph-based Domain Adaptation (ARG-DA), to remedy the aforementioned issues.
Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation
Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of data distributions between the two domains.
Discriminative Label Consistent Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target.
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions.
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain.
Close Yet Distinctive Domain Adaptation
Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.
