iCaps：一个基于解纠缠胶囊网络的可解释分类器
iCaps: An Interpretable Classifier via Disentangled Capsule Networks
摘要：
我们提出了一个可解释的胶囊网络，iCaps，用于图像分类。胶囊是嵌套在每一层中的一组神经元，最后一层中的一个被称为类胶囊，它是一个向量，其范数表示该类的预测概率。使用类胶囊，现有的胶囊网络已经提供了一定程度的可解释性。然而，有两个限制降低了它的可解释性：1）类胶囊也包括分类无关的信息，和2）由类胶囊重叠表示的实体。在这项工作中，我们使用一种新的类监督解纠缠算法和一个额外的正则化，分别解决这两个限制。通过对三个数据集的定量和定性评估，我们证明了所得到的分类器iCaps提供了一个预测，并在其背后提供了明确的理由，而没有性能下降。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
