Towards a general framework for improving the performance of classifiers using XAI methods
This work addresses the problem of computational efficiency for AI researchers by offering an incremental approach to enhance model performance using XAI.
The paper tackles the challenge of improving deep learning classifier performance without retraining by proposing a general framework that uses XAI methods, outlining two learning strategies for implementation.
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.