Summary of Manifold-based Shapley For Sar Recognization Network Explanation, by Xuran Hu et al.
Manifold-based Shapley for SAR Recognization Network Explanation
by Xuran Hu, Mingzhe Zhu, Yuanjing Liu, Zhenpeng Feng, LJubisa Stankovic
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Fusion-Shap method combines game-based explanation techniques with manifold projections to improve the explainability of deep neural networks, particularly in high-risk and high-cost applications like synthetic aperture radar. Building on Shapley’s robust foundations, this approach addresses the limitations of traditional Shapley by projecting high-dimensional features into lower-dimensional manifolds. The resulting Fusion-Shap method enables more accurate and interpretable explanations for complex scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how artificial intelligence works. It introduces a new way to explain how deep neural networks make decisions, which is important because it makes the AI more transparent and trustworthy. Right now, there are problems with traditional methods that try to explain why AI made certain choices. This study solves those problems by using special mathematical tools to simplify complex data and show us what’s really going on inside the AI. |
