LE-CapsNet: A Light and Enhanced Capsule Network

• URL: http://arxiv.org/abs/2511.11708v1
• Date: Wed, 12 Nov 2025 15:45:48 GMT
• Title: LE-CapsNet: A Light and Enhanced Capsule Network
• Authors: Pouya Shiri, Amirali Baniasadi,
• Abstract summary: Capsule Network (CapsNet) has several advantages over CNNs.<n>CapsNet is slow due to its different structure.<n>We propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet.
• Score: 0.07161783472741746
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs. In this work, we propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet. Using 3.8M weights, LECapsNet obtains 76.73% accuracy on the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.3% accuracy on the AffNIST dataset (compared to CapsNet 90.52%).

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