Researchers have developed SnapViT, a novel method for creating elastic Vision Transformers (ViTs) that can adapt to various computational budgets without requiring retraining. This post-pretraining structured pruning technique efficiently combines gradient information with cross-network structure correlations, approximated via an evolutionary algorithm. Experiments on several pretrained models show SnapViT outperforms existing methods across different sparsities, generating adjustable models in under five minutes on a single A100 GPU. AI
IMPACT Enables more flexible deployment of vision models across diverse hardware constraints.
RANK_REASON The cluster contains an academic paper detailing a new method for adapting existing models. [lever_c_demoted from research: ic=1 ai=1.0]
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