A variance-based, retraining-free pruning framework for vision-language models that allocates per-layer sparsity and outperforms Wanda and SparseGPT at high sparsity.
Journal of Artificial Intel- ligence Research (JAIR)55, 409–442 (2016)
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Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
A variance-based, retraining-free pruning framework for vision-language models that allocates per-layer sparsity and outperforms Wanda and SparseGPT at high sparsity.