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-tuning ResNet-18 and DeiT-Small on an imbalanced retinal OCT dataset and CUB-200-2011, matching samples by image identity and holding the split fixed across seeds. Per-sample retention is reproducible: cross-run Spearman correlation of the fitted decay constant is 0.37 to 0.59 over ten seeds. It is architecture-specific: two runs of one backbone agree more than two backbones on identical data (0...
| Authors | Miit Daga, Swarna Priya Ramu |
|---|---|
| Primary category | cs.LG |
| Published | 13 April 2026 |
| Last updated at source | 21 September 2026 |
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