In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from CLIP-based Concept Bottleneck Models (CBMs), our method creates a latent space where each neuron is linked to a specific word. Observing that this latent space can be modeled with simple distributions, we use a Mixture of Gaussians (MoG) formalism to enhance the interpretability of this latent space. Then, we introduce CLIP-QDA, a classifier that only uses statistical values to infer labels from the concepts. In addition, this formalism allows for both local and global explanations. These explanations come from the inner design of our architecture, our work is part of a new family of greybox models, combining performances of opaque foundation models and the interpretability of transparent models. Our empirical findings show that in instances where the MoG assumption holds, CLIP-QDA achieves similar accuracy with state-of-the-art methods CBMs. Our explanations compete with existing XAI methods while being faster to compute.

通过多模式基础模型设计的可解释算法，实现了快速和可解释的图像分类。通过在概念瓶颈模型的基础上建立一个潜空间，将每个神经元与特定的词语关联，利用高斯混合模型增强潜空间的可解释性，引入CLIP-QDA分类器使用统计值来推断概念的标签，同时通过内部设计实现局部和全局解释，并且在MoG假设成立的情况下，与最先进的CBMs方法达到类似的准确性，计算速度更快，并且解释效果与现有的XAI方法相媲美。

CLIP-QDA:一个可解释的概念瓶颈模型