Source-free domain adaptation for semantic image segmentation using internal representations

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic unsupervised domain adaptation (UDA) attempts to address a similar problem when there i...

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Podrobná bibliografie

Hlavní autoři:	Serban Stan, Mohammad Rostami
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2024-06-01
Edice:	Frontiers in Big Data
Témata:	domain adaptation Gaussian mixture model (GMM) optimal transport and Wasserstein distances sliced Wasserstein distance image segmentation
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fdata.2024.1359317/full
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