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

arXiv:2604.11508 · cs.LG, cs.AI · Submitted 2026-04-13 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-04-13

Updated: 2026-09-21

Comments: This manuscript is currently under consideration at Array

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Pruning — 8× in this paper · explained in 4 episodes
• Trace — 8× in this paper · explained in 2 episodes
• Data Pruning — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• An Empirical Study of Example Forgetting during Deep Neural Network Learning

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