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Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints
Self-training based on pseudo-labels has emerged as a dominant approach for addressing conditional distribution shifts in unsupervised domain adaptation (UDA) for semantic segmentation problems.
Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints
Crucially, the objectness constraint is agnostic to the ground-truth semantic segmentation labels and, therefore, remains appropriate for unsupervised adaptation settings.
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