Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping

arXiv:2404.14704v1 Announce Type: new
Abstract: Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land …

architecture arxiv cs.cv cs.ne domain domain adaptation mapping search self-training training type unsupervised