Enhancing Interpretability for Vision Models via Shapley Value Optimization
Published:Dec 16, 2025 12:33
•1 min read
•ArXiv
Analysis
This article, sourced from ArXiv, focuses on improving the interpretability of vision models. The core approach involves using Shapley value optimization, a technique designed to explain the contribution of individual features to a model's output. The research likely explores how this optimization method can make the decision-making process of vision models more transparent and understandable.
Key Takeaways
- •Focuses on improving the interpretability of vision models.
- •Employs Shapley value optimization.
- •Aims to make model decision-making more transparent.
Reference
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