Towards a general framework for improving the performance of classifiers
  using XAI methods
Andrea Apicella Salvatore Giugliano Francesco Isgrò Roberto Prevete
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL)
models, poses challenges in understanding their inner workings by AI
researchers. eXplainable Artificial Intelligence (XAI) inspects internal
mechanisms of AI models providing explanations about their decisions. While
current XAI research predominantly concentrates on explaining AI systems, there
is a growing interest in using XAI techniques to automatically improve the
performance of AI systems themselves. This paper proposes a general framework
for automatically improving the performance of pre-trained DL classifiers using
XAI methods, avoiding the computational overhead associated with retraining
complex models from scratch. In particular, we outline the possibility of two
different learning strategies for implementing this architecture, which we will
call auto-encoder-based and encoder-decoder-based, and discuss their key
aspects.