# Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks

arXiv: https://arxiv.org/abs/2004.07116 · HTML: https://mlsanity.com/paper/2004.07116/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [Towards Efficient Capsule Networks](https://mlsanity.com/paper/2208.09203/) (2022)
- [Shifting Capsule Networks from the Cloud to the Deep Edge](https://mlsanity.com/paper/2110.02911/) (2021)
- [Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations](https://mlsanity.com/paper/2206.10200/) (2022)
- [RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks](https://mlsanity.com/paper/2304.03973/) (2023)

## Abstract (arXiv)

Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior learning capabilities in machine learning tasks, like image classification, compared to the traditional CNNs. However, CapsNets require extremely intense computations and are difficult to be deployed in their original form at the resource-constrained edge devices. This paper makes the first attempt to quantize CapsNet models, to enable their efficient edge implementations, by developing a specialized quantization framework for CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet model for the CIFAR10 dataset, the framework reduces the memory footprint by 6.2x, with only 0.15% accuracy loss. We will open-source our framework at https://git.io/JvDIF in August 2020.