# iCaps: An Interpretable Classifier via Disentangled Capsule Networks

arXiv: https://arxiv.org/abs/2008.08756 · HTML: https://mlsanity.com/paper/2008.08756/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Probabilistic Concept Bottleneck Models](https://mlsanity.com/paper/2306.01574/) (2023)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules](https://mlsanity.com/paper/2105.04836/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism](https://mlsanity.com/paper/2209.11189/) (2022)
- [Explainability and Robustness of Deep Visual Classification Models](https://mlsanity.com/paper/2301.01343/) (2023)
- [Towards the Characterization of Representations Learned via Capsule-based Network Architectures](https://mlsanity.com/paper/2305.05349/) (2023)

## Abstract (arXiv)

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.