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Found 4 papers, 2 papers with code
To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems
The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods.
A comprehensive study on fidelity metrics for XAI
We applied our benchmark to assess the existing fidelity metrics in two different experiments, each using public datasets comprising 52, 000 images.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
A novel approach to generate datasets with XAI ground truth to evaluate image models
With the increased usage of artificial intelligence (AI), it is imperative to understand how these models work internally.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Improving concave point detection to better segment overlapped objects in images
Finally, we obtain a concave point from each region based on the analysis of the relative position of their neighbourhood We experimentally demonstrated that a better concave points detection implies a better cluster division.
