Summary of Sglp: a Similarity Guided Fast Layer Partition Pruning For Compressing Large Deep Models, by Yuqi Li et al.
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
by Yuqi Li, Yao Lu, Zeyu Dong, Chuanguang Yang, Yihao Chen, Jianping Gou
First submitted to arxiv on: 14 Oct 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 The proposed method, Similarity Guided fast Layer Partition pruning (SGLP), addresses the limitations of existing layer pruning methods by focusing on pruning layers within network segments partitioned via representation similarity. SGLP leverages Centered Kernel Alignment (CKA) to indicate internal representations among pre-trained network layers, and then employs Fisher Optimal Segmentation to partition the network into multiple segments for segment-wise layer pruning. The method also innovatively adopts GradNorm for segment-wise layer importance evaluation, eliminating the need for extensive fine-tuning. Experimental results demonstrate that SGLP outperforms state-of-the-art methods in both accuracy and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SGLP is a new way to make deep neural networks smaller and faster without losing their important features. It does this by identifying which layers are most important and removing the less important ones. This helps deep learning models work well on devices with limited resources, like smartphones or edge devices. The method uses special algorithms to analyze the internal workings of the network and then prunes away unnecessary layers. This leads to more accurate predictions while also reducing computational needs. |
Keywords
» Artificial intelligence » Alignment » Deep learning » Fine tuning » Pruning
