What is snapshot pruning in ensemble learning?

Snapshot pruning selects only the most useful checkpoints from snapshot ensembles.

Snapshot pruning improves snapshot ensembles by selecting only high-performing snapshots instead of averaging all saved models. During cyclical learning rate training, multiple local minima are stored. Pruning removes redundant or low-performing snapshots to improve efficiency and sometimes accuracy. This reduces inference cost while retaining ensemble benefits.