Summary of Unified Framework For Neural Network Compression Via Decomposition and Optimal Rank Selection, by Ali Aghababaei-harandi et al.
Unified Framework for Neural Network Compression via Decomposition and Optimal Rank Selection
by Ali Aghababaei-Harandi, Massih-Reza Amini
First submitted to arxiv on: 5 Sep 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 framework addresses the challenge of deploying complex neural networks on resource-constrained devices by presenting a unified approach to simultaneously compress and optimize model size while maintaining accuracy. The framework employs a composite compression loss within defined rank constraints, allowing for automatic rank search in a continuous space without requiring training data. This efficient method maintains the performance of highly compressed models comparable to their original counterparts through a comprehensive analysis on various benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a way to make complex neural networks smaller and more efficient, so they can run on devices like smartphones and embedded systems. The approach uses a new type of compression that combines two techniques: decomposing the model into smaller pieces and finding the best combination of these pieces. This method works without needing any training data and produces results similar to the original models. |
