In the realm of Artificial Intelligence (AI), the importance of Explainable Artificial Intelligence (XAI) is increasingly recognized, particularly as AI models become more integral to our lives. One notable single-instance XAI approach is counterfactual explanation, which aids users in comprehending a model's decisions and offers guidance on altering these decisions. Specifically in the context of image classification models, effective image counterfactual explanations can significantly enhance user understanding. This paper introduces a novel method for computing feature importance within the feature space of a black-box model. By employing information fusion techniques, our method maximizes the use of data to address feature counterfactual explanations in the feature space. Subsequently, we utilize an image generation model to transform these feature counterfactual explanations into image counterfactual explanations. Our experiments demonstrate that the counterfactual explanations generated by our method closely resemble the original images in both pixel and feature spaces. Additionally, our method outperforms established baselines, achieving impressive experimental results.

翻译：在人工智能领域，可解释人工智能的重要性日益凸显，特别是随着AI模型日益融入我们的生活。反事实解释作为一种值得关注的单实例可解释人工智能方法，能够帮助用户理解模型的决策，并提供改变这些决策的指导。特别是在图像分类模型的应用场景中，有效的图像反事实解释可以显著提升用户的理解能力。本文提出了一种在特征空间中计算黑盒模型特征重要性的新方法。通过采用信息融合技术，我们的方法最大限度地利用数据来解决特征空间中的特征反事实解释问题。随后，我们利用图像生成模型将这些特征反事实解释转化为图像反事实解释。实验结果表明，我们的方法生成的反事实解释在像素空间和特征空间均与原始图像高度相似。此外，我们的方法在多项基准测试中超越了现有基线，取得了令人印象深刻的实验结果。