We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a class capsule, which is a vector whose norm indicates a predicted probability for the class. Using the class capsule, existing Capsule Networks already provide some level of interpretability. However, there are two limitations which degrade its interpretability: 1) the class capsule also includes classification-irrelevant information, and 2) entities represented by the class capsule overlap. In this work, we address these two limitations using a novel class-supervised disentanglement algorithm and an additional regularizer, respectively. Through quantitative and qualitative evaluations on three datasets, we demonstrate that the resulting classifier, iCaps, provides a prediction along with clear rationales behind it with no performance degradation.

翻译：我们提出一个可解释的Capsule 网络, iCaps, 用于图像分类。 胶囊是每层内嵌入的一组神经元, 最后一层的是一个叫做类胶囊, 这是一种矢量, 其标准表明该类的预测概率。 使用类胶囊, 现有的 Capsule 网络已经提供了某种程度的可解释性。 但是, 有两种限制可以降低其可解释性:1) 类胶囊还包括与分类无关的信息,2) 类胶囊所代表的实体重叠。 在这项工作中, 我们使用一种新型的、 类监督的分解算法和额外的常规化器分别解决这两个限制。 我们通过对三个数据集的定量和定性评估, 证明由此产生的分类器( iCaps) 提供了一种预测, 以及其背后的清晰原理, 没有性能退化。