Summary of Enhancing Neural Network Interpretability Through Conductance-based Information Plane Analysis, by Jaouad Dabounou and Amine Baazzouz
Enhancing Neural Network Interpretability Through Conductance-Based Information Plane Analysis
by Jaouad Dabounou, Amine Baazzouz
First submitted to arxiv on: 26 Aug 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 new approach uses layer conductance, a measure of sensitivity to input features, to enhance the Information Plane analysis. It provides a more precise characterization of information dynamics within the network by incorporating gradient-based contributions. The proposed method is evaluated on pretrained ResNet50 and VGG16 models using the ImageNet dataset, showing that it can identify critical hidden layers that contribute significantly to model performance and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand how neural networks process information. It uses a measure called layer conductance to see how sensitive each layer is to the input features. This helps to better understand how information flows through the network. The method is tested on some pre-trained models and shows that it can help identify which layers are most important for the model’s performance and understanding. |
