A variance-based, retraining-free pruning framework for vision-language models that allocates per-layer sparsity and outperforms Wanda and SparseGPT at high sparsity.
In: Pro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
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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.