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Domain-Agnostic Prior for Transfer Semantic Segmentation
[article]
2022
arXiv
pre-print
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target domains so that the source-domain features can align with the target-domain semantics. In this paper, we present a simple and effective mechanism that regularizes cross-domain representation learning with a domain-agnostic prior (DAP) that constrains the features extracted from source and target domains to align with a
arXiv:2204.02684v2
fatcat:yione7rdgfazngul3bjecv6xg4