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Found 2 papers, 2 papers with code
On the Model-Agnostic Multi-Source-Free Unsupervised Domain Adaptation
Specifically, we first conduct source model selection based on the proposed selection principles.
Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation
Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics.
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