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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 3·5 min read
CENDRe: Concept Extraction with Natural Domain Representations
TL;DR
CENDRe is a novel concept extraction method for convolutional neural networks (CNNs) that identifies both time and frequency domain patterns driving predictions. It automatically determines the number of concepts and provides accurate localizations, significantly improving the interpretability of CNNs in critical applications.
✦ Why It Matters
Implement CENDRe to enhance model interpretability and reliability in critical time-series applications today.
Key Takeaways
- Utilize CENDRe for improved concept extraction in CNNs.
- Leverage automatic concept determination to streamline analysis.
- Apply frequency domain insights for better fault diagnosis.
- Enhance model interpretability in critical applications.
How It Works
CENDRe operates in two stages: first, it clusters latent representations at each time step to identify concepts, using silhouette-guided aggregation to determine the optimal number of concepts. Second, it localizes these concepts by calculating gradients of a presence score, which contrasts latent representations with their prototypes.
This process generates masks that focus on the most relevant regions in both time and frequency domains, allowing for a comprehensive understanding of the model's decision-making.