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Sarma, R. Raghunatha
- Robustcaps: A Transformation-Robust Capsule Network For Image Classification
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1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India., IN
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2883-2892Abstract
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. To address this issue, we present a deep neural network model that exhibits the desirable property of transformationrobustness. 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.Keywords
Deep Learning, Capsule Networks, Transformation Robustness, Equivariance.
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