There would be the differences in spectra, scale and resolution between the Remote Sensing datasets of the source and target domains, which would lead to the degradation of the cross-domain ...

Source-Free Domain Adaptation (SFDA) addresses the practical need to deploy machine learning models in novel environments without access to the original labelled source data. This paradigm emerges ...

Recently, a research team developed an unsupervised domain adaptation (UDA) approach, the dual domain distribution disruption with semantics preservation (DDSP) framework, achieving high-precision ...

Figure. The advantages of the DDSP framework: (a) Our strategy is to make the model domain-agnostic by exposing it to numerous diverse distributions while preserving semantic information in both ...