UvA-DARE (Digital Academic Repository) Elastic ViTs from Pretrained Models without Retraining
SnapViT: single-shot network approximation for pruned Vision Transformers is introduced, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retraining or labels.
Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort et al.
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