Summary of Reasoning with Trees: Interpreting Cnns Using Hierarchies, by Caroline Mazini Rodrigues (ligm et al.
Reasoning with trees: interpreting CNNs using hierarchies
by Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 a critical challenge in Explainable Artificial Intelligence (xAI), specifically providing faithful and interpretable explanations for neural network reasoning. The existing methods, such as Integrated Gradients and LIME, have limitations, producing noisy maps or deviating from the model’s actual reasoning. To overcome these limitations, the authors introduce xAiTrees, a framework that utilizes hierarchical segmentation techniques to provide multiscale explanations, allowing bias identification and enhancing understanding of neural network decision-making. The proposed approach maintains the model’s reasoning fidelity, offering both human-centric and model-centric segmentation. Experimental results demonstrate that xAiTrees delivers highly interpretable and faithful model explanations, outperforming traditional xAI methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making artificial intelligence more understandable. Right now, it’s hard to figure out why AI models make certain decisions. The authors are trying to solve this problem by creating a new way to explain how neural networks work. They want to show not just what the model decided, but also how it made that decision. This will help people understand AI better and even identify biases in the system. The new approach is called xAiTrees, and it uses special techniques to break down the AI’s reasoning into smaller pieces. By doing this, they can provide more detailed explanations of how the AI arrived at its conclusions. |
Keywords
* Artificial intelligence * Neural network
