Deep Learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on multiple levels of granularity. To this end, we propose a novel hierarchical concept discovery formulation leveraging: (i) recent advances in image-text models, and (ii) an innovative formulation for multi-level concept selection via data-driven and sparsity inducing Bayesian arguments. Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene. As we experimentally show, the proposed construction not only outperforms recent CBM approaches, but also yields a principled framework towards interpetability.

翻译：深度学习算法因其令人瞩目的性能而近期受到广泛关注。然而，其高复杂性和不可解释的运作模式阻碍了其在现实世界安全关键任务中的可靠部署。本研究针对事前可解释性，特别是概念瓶颈模型（CBMs）。我们的目标是设计一个框架，使其在多个粒度层面上，能够以人类可理解的概念为基础，实现高度可解释的决策过程。为此，我们提出了一种新颖的分层概念发现框架，该框架利用：（i）图像-文本模型的最新进展，以及（ii）通过数据驱动和稀疏性诱导贝叶斯论证实现的多层次概念选择创新公式。在此框架中，概念信息不仅仅依赖于整个图像与一般非结构化概念之间的相似性；相反，我们引入了概念层次结构的概念，以揭示并利用图像场景中特定于区域的更细粒度概念信息。正如我们实验所展示的，所提出的结构不仅优于最近的CBM方法，而且为可解释性提供了一个原则性框架。