Summary of Puppet-cnn: Input-adaptive Convolutional Neural Networks with Model Compression Using Ordinary Differential Equation, by Yucheng Xing et al.
Puppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation
by Yucheng Xing, Xin Wang
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel Convolutional Neural Network (CNN) framework called Puppet-CNN, which addresses the limitations of traditional CNN models by adapting the network’s depth and kernel parameters to the input complexity. The proposed framework consists of two modules: a puppet module that processes the input data and a puppeteer module that generates the kernel parameters using Ordinary Differential Equation (ODE). This approach enables significant reduction in model size while maintaining or even improving performance, making it a promising solution for resource-constrained applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new type of Convolutional Neural Network (CNN) called Puppet-CNN. Instead of having a fixed network structure like traditional CNNs, this new framework changes its structure based on the complexity of the input data. This makes it more efficient and powerful than regular CNNs. The researchers tested their method on several datasets and found that it worked better and used less resources than other methods. |
Keywords
» Artificial intelligence » Cnn » Neural network
