Robustcaps: a transformation-robust capsule network for image classification

• URL: http://arxiv.org/abs/2210.11092v1
• Date: Thu, 20 Oct 2022 08:42:33 GMT
• Title: Robustcaps: a transformation-robust capsule network for image classification
• Authors: Sai Raam Venkataraman, S. Balasubramanian, R. Raghunatha Sarma
• Abstract summary: 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. It achieves state-of-the-art accuracies on CIFAR-10, FashionMNIST, and CIFAR-100 datasets.
• Score: 6.445605125467574
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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