Summary of Convex Distillation: Efficient Compression Of Deep Networks Via Convex Optimization, by Prateek Varshney and Mert Pilanci
Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization
by Prateek Varshney, Mert Pilanci
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper addresses the challenge of deploying complex neural networks on resource-constrained edge devices. Traditional compression methods like distillation and pruning often retain non-convexity, making fine-tuning in real-time challenging. The authors introduce a novel distillation technique that uses convex optimization to efficiently compress the model, eliminating intermediate non-convex activation functions. This approach enables label-free data setting and achieves performance comparable to the original model without post-compression fine-tuning. The method is demonstrated on image classification models for multiple standard datasets and outperforms non-convex distillation approaches in data-limited regimes. This promises significant advantages for deploying high-efficiency, low-footprint models on edge devices, making it a practical choice for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer vision algorithms work better on small devices like smartphones or smart cameras. These devices don’t have enough power to run complex computer programs, so we need new ways to make them work faster and more efficiently. The authors created a new way to shrink down big neural networks into smaller ones that still work well without needing lots of data to train them. They tested this method on several popular datasets and showed it works better than older methods in some cases. This could be very useful for using these algorithms in real-world applications like self-driving cars or surveillance cameras. |
Keywords
» Artificial intelligence » Distillation » Fine tuning » Image classification » Optimization » Pruning
