Concept bottleneck models (CBM) are a popular way of creating more
interpretable neural networks by having hidden layer neurons correspond to
human-understandable concepts. However, existing CBMs and their variants have
two crucial limitations: first, they need to collect labeled data for each of
the predefined concepts, which is time consuming and labor intensive; second,
the accuracy of a CBM is often significantly lower than that of a standard
neural network, especially on more complex datasets. This poor performance
creates a barrier for adopting CBMs in practical real world applications.
Motivated by these challenges, we propose Label-free CBM which is a novel
framework to transform any neural network into an interpretable CBM without
labeled concept data, while retaining a high accuracy. Our Label-free CBM has
many advantages, it is: scalable - we present the first CBM scaled to ImageNet,
efficient - creating a CBM takes only a few hours even for very large datasets,
and automated - training it for a new dataset requires minimal human effort.
Our code is available at this https URL

提出了一种新的框架 Label-free CBM，能够将任何神经网络转换成可解释的 CBM，而无需标记的概念数据，且准确性高，并成功地将其扩展到 ImageNet 数据集上，整个创建过程只需要几个小时，未来能在实际的实际场景中大规模应用。

无标记概念瓶颈模型

Label-Free Concept Bottleneck Models

In an attempt to gather a deeper understanding of how convolutional neural
networks (CNNs) reason about human-understandable concepts, we present a method
to infer labeled concept data from hidden layer activations and interpret the
concepts through a shallow decision tree. The decision tree can provide
information about which concepts a model deems important, as well as provide an
understanding of how the concepts interact with each other. Experiments
demonstrate that the extracted decision tree is capable of accurately
representing the original CNN's classifications at low tree depths, thus
encouraging human-in-the-loop understanding of discriminative concepts.

该论文提出了一种从 CNN 隐藏层激活中推断标记的概念数据并通过浅层决策树解释这些概念的方法，这可以提供关于 CNN 模型认为重要的概念以及这些概念如何相互作用的信息，实验表明提取的决策树在树深度较低时能够准确地代表原始 CNN 的分类，从而有助于人们理解有区分力的概念。