Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are generally challenging to obtain, making it crucial to leverage all their prior knowledge. By creating concept-enriched models that incorporate concept information into existing architectures, we exploit their interpretable capabilities to the fullest extent. In particular, we propose Concept-Guided Conditional Diffusion, which can generate visual representations of concepts, and Concept-Guided Prototype Networks, which can create a concept prototype dataset and leverage it to perform interpretable concept prediction. These results open up new lines of research by exploiting pre-existing information in the quest for rendering machine learning more human-understandable.

翻译：基于概念的机器学习方法因提升神经网络可解释性的需求日益增长而愈发重要。然而，概念标注通常难以获取，这使得充分利用其先验知识变得至关重要。通过构建将概念信息融入现有架构的概念增强模型，我们最大限度地开发了其可解释能力。具体而言，我们提出了概念引导条件扩散模型——能够生成概念的视觉表征，以及概念引导原型网络——能够创建概念原型数据集并利用其执行可解释的概念预测。这些成果通过挖掘既有信息来推动机器学习向更易于人类理解的方向发展，从而开辟了新的研究路径。