Summary of Intuitionistic Fuzzy Cognitive Maps For Interpretable Image Classification, by Georgia Sovatzidi et al.
Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification
by Georgia Sovatzidi, Michael D. Vasilakakis, Dimitris K. Iakovidis
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Interpretable Intuitionistic FCM (I2FCM) framework offers a novel approach to making Convolutional Neural Network (CNN) models interpretable. By applying iFCMs to image classification tasks, the model can assess the quality of its output through the estimation of hesitancy, a concept similar to human hesitation in decision making. The I2FCM framework consists of feature extraction, learning algorithm for intuitionistic fuzzy interconnections, and an inherently interpretable classification approach based on image contents. Experimental results on publicly available datasets show that I2FCM provides enhanced classification performance while offering interpretable inferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary I2FCM is a new way to make computer vision models understandable. It uses a special type of machine learning called iFCMs, which can estimate how sure the model is about its predictions. This helps us trust the model’s decisions more. The I2FCM framework has three parts: finding important features in images, learning how to combine those features, and making decisions based on image contents. Tests on public datasets show that I2FCM works well and gives us a better understanding of what it’s thinking. |
Keywords
* Artificial intelligence * Classification * Cnn * Feature extraction * Image classification * Machine learning * Neural network
