# Quick-CapsNet (QCN): A fast alternative to Capsule Networks

arXiv: https://arxiv.org/abs/2510.07600 · HTML: https://mlsanity.com/paper/2510.07600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [DeepCaps: Going Deeper with Capsule Networks](https://mlsanity.com/paper/1904.09546/) (2019)
- [Fast CapsNet for Lung Cancer Screening](https://mlsanity.com/paper/1806.07416/) (2018)
- [The Multi-Lane Capsule Network (MLCN)](https://mlsanity.com/paper/1902.08431/) (2019)
- [A Capsule Network for Traffic Speed Prediction in Complex Road Networks](https://mlsanity.com/paper/1807.10603/) (2018)

## Built on by (most cited first, 3 of 3)

- [Convolutional Fully-Connected Capsule Network (CFC-CapsNet): A Novel and Fast Capsule Network](https://mlsanity.com/paper/2511.05617/) (2025)
- [LE-CapsNet: A Light and Enhanced Capsule Network](https://mlsanity.com/paper/2511.11708/) (2025)
- [DL-CapsNet: A Deep and Light Capsule Network](https://mlsanity.com/paper/2512.00061/) (2025)

## Abstract (arXiv)

The basic computational unit in Capsule Network (CapsNet) is a capsule (vs. neurons in Convolutional Neural Networks (CNNs)). A capsule is a set of neurons, which form a vector. CapsNet is used for supervised classification of data and has achieved state-of-the-art accuracy on MNIST digit recognition dataset, outperforming conventional CNNs in detecting overlapping digits. Moreover, CapsNet shows higher robustness towards affine transformation when compared to CNNs for MNIST datasets. One of the drawbacks of CapsNet, however, is slow training and testing. This can be a bottleneck for applications that require a fast network, especially during inference. In this work, we introduce Quick-CapsNet (QCN) as a fast alternative to CapsNet, which can be a starting point to develop CapsNet for fast real-time applications. QCN builds on producing a fewer number of capsules, which results in a faster network. QCN achieves this at the cost of marginal loss in accuracy. Inference is 5x faster on MNIST, F-MNIST, SVHN and Cifar-10 datasets. We also further enhanced QCN by employing a more powerful decoder instead of the default decoder to further improve QCN.