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Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers
Fine-tuning a pretrained classifier leaves some samples reliably learned and others cycling between correct and incorrect. Curriculum learning, data pruning and dataset cartography assume that pattern is a property of the sample, untested. We record per-sample correctness at every epoch while fine-t
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