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Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation
[article]
2022
arXiv
pre-print
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefore, the domain adaptation can benefit from a subset of source data composed solely of helpful and relevant samples. The proposed method
arXiv:2205.00312v2
fatcat:aaclo4aktzg5nb2tm22pjnecjy