17 citations · 18 across the 4 of their papers we have counts for
4 papers
IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +2
The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However,…
Towards a general framework for improving the performance of classifiers using XAI methods
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial…
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine…
A general approach to compute the relevance of middle-level input features
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) mode…