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Found 3 papers, 2 papers with code
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.
Joint Contrastive Learning for Unsupervised Domain Adaptation
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
We propose a novel domain adaptation method based on label propagation and cycle consistency to let the clusters of the features from the two domains overlap exactly and become clear for high accuracy.
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