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Found 3 papers, 1 papers with code
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation
Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints.
Unsupervised Class-Incremental Learning Through Confusion
While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training.
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label distribution shift.
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