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Domain Adaptive Semantic Segmentation without Source Data
[article]
2021
arXiv
pre-print
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many real-world applications, such as automatic pilot. However, large amounts of source domain data often introduce significant costs in storage and training, and sometimes the source data is inaccessible due to privacy policies. To address these problems, we investigate domain adaptive semantic segmentation without
arXiv:2110.06484v1
fatcat:dd3kxoqrvnaefahpajeol6pjei