This paper addresses the challenge of generating Counterfactual Explanations (CEs), involving the identification and modification of the fewest necessary features to alter a classifier's prediction for a given image. Our proposed method, Text-to-Image Models for Counterfactual Explanations (TIME), is a black-box counterfactual technique based on distillation. Unlike previous methods, this approach requires solely the image and its prediction, omitting the need for the classifier's structure, parameters, or gradients. Before generating the counterfactuals, TIME introduces two distinct biases into Stable Diffusion in the form of textual embeddings: the context bias, associated with the image's structure, and the class bias, linked to class-specific features learned by the target classifier. After learning these biases, we find the optimal latent code applying the classifier's predicted class token and regenerate the image using the target embedding as conditioning, producing the counterfactual explanation. Extensive empirical studies validate that TIME can generate explanations of comparable effectiveness even when operating within a black-box setting.

翻译：本文解决了生成反事实解释的挑战，涉及识别并修改最少数量的必要特征以改变分类器对给定图像的预测。我们提出的方法——基于文本到图像模型的反事实解释（TIME），是一种基于蒸馏的黑盒反事实技术。与先前方法不同，本方法仅需图像及其预测结果，无需分类器的结构、参数或梯度。在生成反事实之前，TIME以文本嵌入的形式向Stable Diffusion引入两类不同的偏差：与图像结构相关的上下文偏差，以及与目标分类器学习到的类别特定特征相关的类别偏差。学习这些偏差后，我们通过应用分类器预测的类别标记找到最优潜在编码，并以目标嵌入作为条件重新生成图像，从而产生反事实解释。广泛的实证研究验证了TIME在黑盒设置下也能生成效果相当的解释。