Summary of Rethinking Pruning For Vision-language Models: Strategies For Effective Sparsity and Performance Restoration, by Shwai He et al.
Rethinking Pruning for Vision-Language Models: Strategies for Effective Sparsity and Performance Restoration
by Shwai He, Ang Li, Tianlong Chen
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study proposes a novel approach to compressing Vision-Language Models (VLMs) by pruning and finetuning, achieving significant improvements in performance while reducing computational requirements. The researchers investigate two key challenges: effectively distributing sparsity across modality-specific models and restoring the performance of pruned sparse VLMs. They identify two effective pruning settings and develop SparseLoRA, a method that applies sparsity directly to LoRA weights. Experimental results demonstrate an 11.3% boost under 2:4 sparsity and a 47.6% enhancement under unstructured 70% sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale Vision-Language Models (VLMs) can do amazing things, like understand pictures and words together! But making these models work on devices with limited power is really hard. Scientists tried pruning some parts of the model to make it smaller, but that didn’t quite work as expected. To fix this, they came up with a new idea called SparseLoRA. It helps the pruned model remember what it learned before, so it can still do its job well. The results are impressive, showing a big boost in performance when using less power. |
Keywords
* Artificial intelligence * Lora * Pruning
