论文标题
具有其他无监督概念的概念瓶颈模型
Concept Bottleneck Model with Additional Unsupervised Concepts
论文作者
论文摘要
随着对问责制的需求不断提高，可解释性正成为现实世界中AI应用程序的重要能力。但是，大多数方法都利用事后方法而不是训练可解释的模型。在本文中，我们提出了一个基于概念瓶颈模型（CBM）的新颖的可解释模型。 CBM使用概念标签来训练中间层作为附加的可见层。但是，由于概念标签的数量限制了该层的维度，因此很难使用少数标签获得高精度。为了解决这个问题，我们将受监督的概念与接受自我解释神经网络（SENN）的无监督概念整合在一起。通过在减少计算量的同时，通过无缝训练这两种概念，我们可以同时获得监督和无监督的概念，即使是大型图像。我们将提出的模型称为具有其他无监督概念（CBM-AUC）的概念瓶颈模型。我们通过实验证实，所提出的模型的表现优于CBM和SENN。我们还可以看到每个概念的显着性图，并确认它与语义含义一致。
With the increasing demands for accountability, interpretability is becoming an essential capability for real-world AI applications. However, most methods utilize post-hoc approaches rather than training the interpretable model. In this article, we propose a novel interpretable model based on the concept bottleneck model (CBM). CBM uses concept labels to train an intermediate layer as the additional visible layer. However, because the number of concept labels restricts the dimension of this layer, it is difficult to obtain high accuracy with a small number of labels. To address this issue, we integrate supervised concepts with unsupervised ones trained with self-explaining neural networks (SENNs). By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC). We experimentally confirmed that the proposed model outperformed CBM and SENN. We also visualized the saliency map of each concept and confirmed that it was consistent with the semantic meanings.
