Abstract
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.
Abstract (translated)
可解释人工智能(XAI)领域的目标是改善黑盒机器学习模型的可解释性。基于输入特征重要性值构建热图是一种常见的方法,用于解释这些模型产生预测背后的基本函数。热图几乎可以向人类解释,但仍然有一些缺点。非专家用户可能无法完全理解热图的逻辑(热图的逻辑是在模型预测相关的像素以不同强度或颜色强调的逻辑)。此外,输入图像中与模型预测相关的物体和区域往往无法通过热图完全区分。在本文中,我们提出了一个框架称为TbExplain,采用XAI技术和预先训练的对象检测器,以呈现场景分类模型的文本解释。此外,TbExplain还包括一种新的方法来纠正预测,并基于输入图像中物体的统计信息文本解释它们,当最初的预测不可靠时。为了评估文本解释的可靠性和有效性,我们进行了一种定性实验,结果表明这些解释足够可靠。此外,我们对TbExplain与场景分类数据集的定量和定性实验表明, ResNet变体的分类精度有了提高。
URL
https://arxiv.org/abs/2307.10003
