Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning
Parameter-efficient fine-tuning often selects trainable parameters before adaptation using architectural heuristics, without accounting for their varying importance during training. We introduce \textbf{FisherAdapTune}, which progressively selects parameter groups based on temporal drift in their Fisher information. Under a local Gaussian approximation, we bound the divergence between the fine-tuned posterior and pretrained prior by accumulated Fisher-weighted update costs, motivating curvature-aware selection. FisherAdapTune measures Jensen-Shannon distance between successive Fisher-value distributions and uses an adaptive threshold to freeze stabilized groups. Across VTAB-1k classificat...
| Authors | Ghodsiyeh Rostami, Po-Han Chen, Mahdi S. Hosseini |
|---|---|
| Primary category | cs.CV |
| Published | 8 June 2026 |
| Last updated at source | 28 September 2026 |
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