2 citations · 3 across the 3 of their papers we have counts for
4 papers
ShapBPT: Image Feature Attributions Using Data-Aware Binary Partition Trees
Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari +1
Pixel-level feature attributions are an important tool in eXplainable AI for Computer Vision (XCV), providing visual insights into how image features influence model predictions. T…
Enhancing interpretability of rule-based classifiers through feature graphs
Christel Sirocchi, Damiano Verda
In domains where transparency and trustworthiness are crucial, such as healthcare, rule-based systems are widely used and often preferred over black-box models for decision support…
Can I trust my anomaly detection system? A case study based on explainable AI
Muhammad Rashid, Elvio Amparore, Enrico Ferrari +1
Generative models based on variational autoencoders are a popular technique for detecting anomalies in images in a semi-supervised context. A common approach employs the anomaly sc…
Using Stratified Sampling to Improve LIME Image Explanations
Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari +1
We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts…