Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers.

arXiv:2604.11508v3 Announce Type: replace-cross Abstract: 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 i