TbExplain: A Text-based Explanation Method for Scene Classification
  Models with the Statistical Prediction Correction
Amirhossein Aminimehr Pouya Khani Amirali Molaei Amirmohammad Kazemeini Erik Cambria
The field of Explainable Artificial Intelligence (XAI) aims to improve the
interpretability of black-box machine learning models. Building a heatmap based
on the importance value of input features is a popular method for explaining
the underlying functions of such models in producing their predictions.
Heatmaps are almost understandable to humans, yet they are not without flaws.
Non-expert users, for example, may not fully understand the logic of heatmaps
(the logic in which relevant pixels to the model's prediction are highlighted
with different intensities or colors). Additionally, objects and regions of the
input image that are relevant to the model prediction are frequently not
entirely differentiated by heatmaps. In this paper, we propose a framework
called TbExplain that employs XAI techniques and a pre-trained object detector
to present text-based explanations of scene classification models. Moreover,
TbExplain incorporates a novel method to correct predictions and textually
explain them based on the statistics of objects in the input image when the
initial prediction is unreliable. To assess the trustworthiness and validity of
the text-based explanations, we conducted a qualitative experiment, and the
findings indicated that these explanations are sufficiently reliable.
Furthermore, our quantitative and qualitative experiments on TbExplain with
scene classification datasets reveal an improvement in classification accuracy
over ResNet variants.