用概念增强扩散与原型网络，让模型生成可解释的视觉表示。
Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks
- 通过概念引导扩散模型生成概念视觉表征
- 构建概念原型数据集实现可解释预测
- 适合关注模型可解释性的研究者
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而日益重要。然而，概念标注通常难以获取，因此充分利用已有先验知识至关重要。通过将概念信息融入现有架构，构建概念增强模型，以最大化其可解释能力。我们提出概念引导的条件扩散模型，可生成概念的视觉表示；以及概念引导的原型网络，可创建概念原型数据集并用于可解释的概念预测。这些成果为利用现有知识提升机器学习的人类可理解性开辟了新路径。
原文摘要 · Abstract (English)
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.
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