# DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsing

arXiv: https://arxiv.org/abs/2104.14735 · HTML: https://mlsanity.com/paper/2104.14735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Stacked Capsule Autoencoders](https://mlsanity.com/paper/1906.06818/) (2019)
- [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https://mlsanity.com/paper/1809.03627/) (2018)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [Capsule Routing via Variational Bayes](https://mlsanity.com/paper/1905.11455/) (2019)
- [CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing](https://mlsanity.com/paper/1910.00132/) (2019)
- [CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces](https://mlsanity.com/paper/1805.07621/) (2018)
- [Subspace Capsule Network](https://mlsanity.com/paper/2002.02924/) (2020)

## Built on by (most cited first, 2 of 2)

- [Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy](https://mlsanity.com/paper/2411.00868/) (2024)
- [Deep Sprite-based Image Models: An Analysis](https://mlsanity.com/paper/2604.19480/) (2026)

## Abstract (arXiv)

Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks. This paper proposes a simple and effective capsule autoencoder to address this issue, called DPR-CAE. In our approach, the encoder parses the input into a set of part capsules, including pose, intensity, and dynamic vector. The decoder introduces a novel dynamic part representation (DPR) by combining the dynamic vector and a shared template bank. These part representations are then regulated by corresponding capsules to composite the final output in an interpretable way. Besides, an extra translation-invariant module is proposed to avoid directly learning the uncertain scene-part relationship in our DPR-CAE, which makes the resulting method achieves a promising performance gain on rm-MNIST and rm-Fashion-MNIST. % to model the scene-object relationship DPR-CAE can be easily combined with the existing stacked capsule autoencoder and experimental results show it significantly improves performance in terms of unsupervised object classification. Our code is available in the Appendix.