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Towards a general framework for improving the performance of classifiers using XAI methods
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers.
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems
We start from the hypothesis that some autoencoders, relying on standard data representation approaches, could extract more salient and understandable input properties, which we call here \textit{Middle-Level input Features} (MLFs), for a user with respect to raw low-level features.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
A general approach to compute the relevance of middle-level input features
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) models in terms of middle-level features.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
