# Group-based Learning of Disentangled Representations with Generalizability for Novel Contents

arXiv: https://arxiv.org/abs/1809.02383 · HTML: https://mlsanity.com/paper/1809.02383/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Unity by Diversity: Improved Representation Learning in Multimodal VAEs](https://mlsanity.com/paper/2403.05300/) (2024)
- [Learning Group Importance using the Differentiable Hypergeometric Distribution](https://mlsanity.com/paper/2203.01629/) (2022)
- [Differentiable Random Partition Models](https://mlsanity.com/paper/2305.16841/) (2023)
- [Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE](https://mlsanity.com/paper/2203.16037/) (2022)

## Abstract (arXiv)

Sensory data are often comprised of independent content and transformation factors. For example, face images may have shapes as content and poses as transformation. To infer separately these factors from given data, various ``disentangling'' models have been proposed. However, many of these are supervised or semi-supervised, either requiring attribute labels that are often unavailable or disallowing for generalization over new contents. In this study, we introduce a novel deep generative model, called group-based variational autoencoders. In this, we assume no explicit labels, but a weaker form of structure that groups together data instances having the same content but transformed differently; we thereby separately estimate a group-common factor as content and an instance-specific factor as transformation. This approach allows for learning to represent a general continuous space of contents, which can accommodate unseen contents. Despite the simplicity, our model succeeded in learning, from five datasets, content representations that are highly separate from the transformation representation and generalizable to data with novel contents. We further provide detailed analysis of the latent content code and show insight into how our model obtains the notable transformation invariance and content generalizability.