Multimodal Variational Autoencoder: a Barycentric View

• URL: http://arxiv.org/abs/2412.20487v1
• Date: Sun, 29 Dec 2024 15:02:50 GMT
• Title: Multimodal Variational Autoencoder: a Barycentric View
• Authors: Peijie Qiu, Wenhui Zhu, Sayantan Kumar, Xiwen Chen, Xiaotong Sun, Jin Yang, Abolfazl Razi, Yalin Wang, Aristeidis Sotiras,
• Abstract summary: We provide an alternative generic and theoretical formulation of multimodal VAE through the lens of barycenter.<n>In particular, we explore the Wasserstein barycenter defined by the 2-Wasserstein distance, which better preserves the geometry of unimodal distributions.<n> Empirical studies on three multimodal benchmarks demonstrated the effectiveness of the proposed method.
• Score: 3.413330490927693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary goal of these models is to learn a modality-invariant and modality-specific representation that characterizes information across multiple modalities. Previous attempts at multimodal VAEs approach this mainly through the lens of experts, aggregating unimodal inference distributions with a product of experts (PoE), a mixture of experts (MoE), or a combination of both. In this paper, we provide an alternative generic and theoretical formulation of multimodal VAE through the lens of barycenter. We first show that PoE and MoE are specific instances of barycenters, derived by minimizing the asymmetric weighted KL divergence to unimodal inference distributions. Our novel formulation extends these two barycenters to a more flexible choice by considering different types of divergences. In particular, we explore the Wasserstein barycenter defined by the 2-Wasserstein distance, which better preserves the geometry of unimodal distributions by capturing both modality-specific and modality-invariant representations compared to KL divergence. Empirical studies on three multimodal benchmarks demonstrated the effectiveness of the proposed method.

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