# Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations

arXiv: https://arxiv.org/abs/2012.13024 · HTML: https://mlsanity.com/paper/2012.13024/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [A survey of multimodal deep generative models](https://mlsanity.com/paper/2207.02127/) (2022)
- [A multimodal dynamical variational autoencoder for audiovisual speech representation learning](https://mlsanity.com/paper/2305.03582/) (2023)
- [Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders](https://mlsanity.com/paper/2505.01134/) (2025)
- [Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference](https://mlsanity.com/paper/2410.11403/) (2024)
- [Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering](https://mlsanity.com/paper/2504.04676/) (2025)
- [H"older++: Improving the Quality-Coherence Trade-off in Multimodal VAEs](https://mlsanity.com/paper/2606.13381/) (2026)

## Abstract (arXiv)

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We specifically consider the instance where the latent factor may be of both continuous and discrete nature, leading to the family of general hybrid DMVAE models. We demonstrate the utility of DMVAE on a semi-supervised learning task, where one of the modalities contains partial data labels, both relevant and irrelevant to the other modality. Our experiments on several benchmarks indicate the importance of the private-shared disentanglement as well as the hybrid latent representation.