# DeepCaps: Going Deeper with Capsule Networks

arXiv: https://arxiv.org/abs/1904.09546 · HTML: https://mlsanity.com/paper/1904.09546/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2019.

## Built on by (most cited first, 30 of 42)

- [Conditional Variational Capsule Network for Open Set Recognition](https://mlsanity.com/paper/2104.09159/) (2021)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [Effective and Efficient Vote Attack on Capsule Networks](https://mlsanity.com/paper/2102.10055/) (2021)
- [Capsule Network is Not More Robust than Convolutional Network](https://mlsanity.com/paper/2103.15459/) (2021)
- [An Algorithm for Routing Vectors in Sequences](https://mlsanity.com/paper/2211.11754/) (2022)
- [Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption](https://mlsanity.com/paper/2301.01583/) (2023)
- [OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning](https://mlsanity.com/paper/2403.13351/) (2024)
- [Training Deep Capsule Networks with Residual Connections](https://mlsanity.com/paper/2104.07393/) (2021)
- [RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks](https://mlsanity.com/paper/2210.05276/) (2022)
- [Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network](https://mlsanity.com/paper/2208.00594/) (2022)
- [Towards Efficient Capsule Networks](https://mlsanity.com/paper/2208.09203/) (2022)
- [Energy-Aware Deep Learning on Resource-Constrained Hardware](https://mlsanity.com/paper/2505.12523/) (2025)
- [Quick-CapsNet (QCN): A fast alternative to Capsule Networks](https://mlsanity.com/paper/2510.07600/) (2025)
- [Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations](https://mlsanity.com/paper/2101.06848/) (2021)
- [Capsule Networks Do Not Need to Model Everything](https://mlsanity.com/paper/2204.01298/) (2022)
- [Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations](https://mlsanity.com/paper/2206.10200/) (2022)
- [ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2104.12085/) (2021)
- [Signature-Graph Networks](https://mlsanity.com/paper/2110.11551/) (2021)
- [PointCaps: Raw Point Cloud Processing using Capsule Networks with Euclidean Distance Routing](https://mlsanity.com/paper/2112.11258/) (2021)
- [β-CapsNet: Learning Disentangled Representation for CapsNet by Information Bottleneck](https://mlsanity.com/paper/2209.05239/) (2022)
- [RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks](https://mlsanity.com/paper/2304.03973/) (2023)
- [Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks](https://mlsanity.com/paper/2309.06438/) (2023)
- [ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis](https://mlsanity.com/paper/2411.01564/) (2024)
- [WideCaps: A Wide Attention based Capsule Network for Image Classification](https://mlsanity.com/paper/2108.03627/) (2021)
- [CapsNet for Medical Image Segmentation](https://mlsanity.com/paper/2203.08948/) (2022)
- [ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing Mechanism](https://mlsanity.com/paper/2203.15547/) (2022)
- [Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning](https://mlsanity.com/paper/2206.13465/) (2022)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Robustcaps: a transformation-robust capsule network for image classification](https://mlsanity.com/paper/2210.11092/) (2022)

## Abstract (arXiv)

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.