Summary of Compression Repair For Feedforward Neural Networks Based on Model Equivalence Evaluation, by Zihao Mo et al.
Compression Repair for Feedforward Neural Networks Based on Model Equivalence Evaluation
by Zihao Mo, Yejiang Yang, Shuaizheng Lu, Weiming Xiang
First submitted to arxiv on: 18 Feb 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 method for repairing compressed Feedforward Neural Networks (FNNs) by evaluating the equivalence between two neural networks. The approach develops a new neural network equivalence evaluation method that computes the output discrepancy between two networks, allowing it to quantify the output difference produced by compression procedures. By initializing a training set based on this computed discrepancy and re-training the compressed FNN, the paper demonstrates the effectiveness of its proposed repair method on the MNIST dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing mistakes in special kinds of computer programs called Neural Networks (like those used for recognizing pictures). When we shrink these networks to make them work faster, they don’t always perform as well. The researchers developed a new way to measure how different two neural networks are from each other. They use this measurement to create a “training set” that helps the shrunk network work better. This method was tested on a big dataset of pictures (called MNIST) and showed good results. |
Keywords
* Artificial intelligence * Neural network
