Source-Free Time-Series Domain Adaptation With Prior Evaluation of Model Salience

Summary

The paper proposes PrEPoA, a fine-tuning paradigm for source-free domain adaptation on time-series data that combines prior evaluation of model salience with posterior adaptation. Model salience is treated as a quantifiable proxy of semantic interpretability, measured by a key pattern reconstruction module and calibrated with an interpattern triplet loss. The authors report that PrEPoA outperforms nine UDA and seven SFDA methods across five datasets and works as a plug-and-play module for other SFDA methods.