Summary of Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks, by Xuran Hu et al.
Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
by Xuran Hu, Mingzhe Zhu, Zhenpeng Feng, Miloš Daković, Ljubiša Stanković
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed method tackles the “black box” problem in deep neural networks (DNNs) by introducing a perturbation-based interpretation guided by feature coalitions. This approach leverages deep information about the network to extract correlated features, addressing the limitations of existing methods that neglect feature dependencies. The technique is validated through both quantitative and qualitative experiments, showcasing its effectiveness in improving transparency and reliability. The proposed consistency loss is carefully designed to guide network interpretation, further enhancing the method’s performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem with artificial intelligence (AI) called the “black box” issue. Deep neural networks are like super powerful computers that can learn from data, but we don’t always understand how they make their decisions. This is bad because we need AI to be transparent and trustworthy. The authors create a new way to interpret these networks by looking at how different parts of the network work together. They test this method and show it works better than other approaches. This research can help us build more reliable AI systems. |
