# Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators

arXiv: https://arxiv.org/abs/1905.12867 · HTML: https://mlsanity.com/paper/1905.12867/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 5 of 5)

- [A survey of multimodal deep generative models](https://mlsanity.com/paper/2207.02127/) (2022)
- [Maximum Likelihood Estimation for Multimodal Learning with Missing Modality](https://mlsanity.com/paper/2108.10513/) (2021)
- [From Concrete to Abstract: A Multimodal Generative Approach to Abstract Concept Learning](https://mlsanity.com/paper/2410.02365/) (2024)
- [Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization](https://mlsanity.com/paper/2205.02670/) (2022)
- [Template Collapse and Information-Theoretic Limits in Camera rPPG Pulse Morphology Restoration](https://mlsanity.com/paper/2606.03802/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders and variational associators. The variational associators transfer the latent spaces between auto-encoders that represent different modalities. The proposed structure successfully associates even heterogeneous modal data and easily incorporates the additional modality to the entire network via the proposed cross-modal associator. Furthermore, the proposed structure can be trained with only a small amount of paired data since auto-encoders can be trained by unsupervised manner. Through experiments, the effectiveness of the proposed structure is validated on various datasets including visual and auditory data.