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

Source: [Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks](<https://www.sota2.com/research/paper/68fa692b1e209131cea207f0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:54:51.606Z

Authors: Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Maurizio Martina, Guido Masera, Muhammad Shafique

Published: 2020-04-15T14:32:45.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.07116>)

[PDF](<https://arxiv.org/pdf/2004.07116.pdf>)