DRIVE weights fine-tuning samples to limit forgetting without source data
Per-sample weights combine Shapley anti-forgetting and adaptation signals with diversity and a prior.
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Per-sample weights combine Shapley anti-forgetting and adaptation signals with diversity and a prior.
On HLS Burn Scar, SSI beat an MC-Owen estimator while using about 6% of its evaluation budget.
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