Core Concepts
Using XAI methods to enhance pre-trained DL classifiers' performance.
Abstract
The paper introduces a framework to automatically enhance the performance of pre-trained Deep Learning (DL) classifiers using eXplainable Artificial Intelligence (XAI) methods. It aims to bridge the gap between XAI and model performance enhancement by integrating explanations with classifier outputs. Two learning strategies, auto-encoder-based and encoder-decoder-based, are outlined for this purpose. The proposed approach focuses on improving already trained ML models without extensive retraining, leveraging XAI techniques effectively.
Stats
"arXiv:2403.10373v1 [cs.LG] 15 Mar 2024"
Quotes
"A notable aspect of our approach is that we can directly obtain the explanation of the model responses without using an XAI method."
"It would be valuable to investigate the impact of the proposed approach on the automatic improvement of an already trained ML model."
"Investigating the robustness of our approach with respect to a modification of the original dataset used to train the model M is certainly a relevant aspect."