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Found 7 papers, 1 papers with code
MOT: Masked Optimal Transport for Partial Domain Adaptation
A novel masked OT (MOT) methodology on conditional distributions is proposed by defining a mask operation with label information.
Maximizing Conditional Independence for Unsupervised Domain Adaptation
Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions.
Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm
As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment.
Conditional Bures Metric for Domain Adaptation
A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA.
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Second, batch-wise training of deep learning limits the characterization of the global structure.
Discriminative Residual Analysis for Image Set Classification with Posture and Age Variations
Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations.
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.
