Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning
Researchers introduce FisherAdapTune, a machine learning framework that dynamically selects which parameters to fine-tune in pretrained models by monitoring Fisher information geometry rather than relying on fixed architectural rules. The method demonstrates improved performance and zero-shot transfer capabilities on segmentation tasks while reducing computational overhead.