# Robustcaps: a transformation-robust capsule network for image classification

Source: [Robustcaps: a transformation-robust capsule network for image classification](<https://www.sota2.com/research/paper/68fa6dbd1e209131cea3bc12>)

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

Data updated: 2025-10-26T00:42:13.334Z

Authors: Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma

Published: 2022-10-20T08:42:33.000Z

## Abstract

Geometric transformations of the training data as well as the test data present challenges to the use of deep neural networks to vision-based learning tasks. In order to address this issue, we present a deep neural network model that exhibits the desirable property of transformation-robustness. Our model, termed RobustCaps, uses group-equivariant convolutions in an improved capsule network model. RobustCaps uses a global context-normalised procedure in its routing algorithm to learn transformation-invariant part-whole relationships within image data. This learning of such relationships allows our model to outperform both capsule and convolutional neural network baselines on transformation-robust classification tasks. Specifically, RobustCaps achieves state-of-the-art accuracies on CIFAR-10, FashionMNIST, and CIFAR-100 when the images in these datasets are subjected to train and test-time rotations and translations.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.11092>)

[PDF](<https://arxiv.org/pdf/2210.11092.pdf>)