Traditional tabular classifiers provide explainable decision-making with interpretable features(concepts). However, using their explainability in vision tasks has been limited due to the pixel representation of images. In this paper, we design Img2Tabs that classify images by concepts to harness the explainability of tabular classifiers. Img2Tabs encode image pixels into tabular features by StyleGAN inversion. Since not all of the resulting features are class-relevant or interpretable due to their generative nature, we expect Img2Tab classifiers to discover class-relevant concepts automatically from the StyleGAN features. Thus, we propose a novel method using the Wasserstein-1 metric to quantify class-relevancy and interpretability simultaneously. Using this method, we investigate whether important features extracted by tabular classifiers are class-relevant concepts. Consequently, we determine the most effective classifier for Img2Tabs in terms of discovering class-relevant concepts automatically from StyleGAN features. In evaluations, we demonstrate concept-based explanations through importance and visualization. Img2Tab achieves top-1 accuracy that is on par with CNN classifiers and deep feature learning baselines. Additionally, we show that users can easily debug Img2Tab classifiers at the concept level to ensure unbiased and fair decision-making without sacrificing accuracy.

翻译：传统表格分类器利用可解释特征（概念）提供可解释的决策。然而，由于图像的像素表示，其在视觉任务中的可解释性应用受到限制。本文设计了Img2Tab，通过概念对图像进行分类，以利用表格分类器的可解释性。Img2Tab通过StyleGAN反演将图像像素编码为表格特征。由于生成模型的特性，并非所有特征都类别相关或可解释，因此我们期望Img2Tab分类器能够从StyleGAN特征中自动发现类别相关概念。为此，我们提出了一种基于Wasserstein-1度量同时量化类别相关性和可解释性的新方法。利用该方法，我们探究了表格分类器提取的重要特征是否为类别相关概念。最终，我们确定了在从StyleGAN特征中自动发现类别相关概念方面最有效的Img2Tab分类器。在评估中，我们通过重要性和可视化展示了基于概念的解释。Img2Tab实现了与CNN分类器和深度特征学习基线相当的前1准确率。此外，我们表明用户可以在概念层面轻松调试Img2Tab分类器，以确保在无需牺牲准确性的情况下实现无偏和公平的决策。