Summary of Mope-clip: Structured Pruning For Efficient Vision-language Models with Module-wise Pruning Error Metric, by Haokun Lin et al.
MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
by Haokun Lin, Haoli Bai, Zhili Liu, Lu Hou, Muyi Sun, Linqi Song, Ying Wei, Zhenan Sun
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 This research paper proposes a novel approach to compressing vision-language pre-trained (VLP) models, which are crucial for various downstream tasks. The authors demonstrate that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. To address this issue, they introduce the Module-wise Pruning Error (MoPE) metric, which accurately assesses CLIP module importance by measuring the decline in performance on cross-modal tasks. The proposed unified pruning framework is applicable to both pre-training and task-specific fine-tuning compression stages. Experimental results show that MoPE-CLIP significantly reduces pre-training costs while maintaining strong zero-shot capabilities, outperforming previous state-of-the-art VLP compression methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make big language models smaller without losing their ability to perform well on different tasks. The current methods for compressing these models are limited because they don’t take into account how important each part of the model is. To fix this, the researchers created a new way to measure how important each part of the model is and used it to create a new method that can make both big and small language models perform well on different tasks. |
Keywords
» Artificial intelligence » Fine tuning » Pruning » Zero shot
