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Found 4 papers, 1 papers with code
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e. g., photographs) and unlabeled data from a target domain (e. g., sketches) are used to learn a classifier for the target domain.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
How does Contrastive Pre-training Connect Disparate Domains?
We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart.
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks.
