Stabilizing Multimodal Autoencoders: A Theoretical and Empirical Analysis of Fusion Strategies

TL;DR

This paper combines theoretical analysis and empirical experiments to improve the stability and performance of multimodal autoencoders through a novel fusion strategy based on Lipschitz properties.

Contribution

It introduces a regularized attention-based fusion method derived from Lipschitz analysis, enhancing training stability and outperforming existing strategies.

Findings

Theoretical Lipschitz constants for fusion methods were derived.

The proposed fusion method improves stability and convergence.

Empirical results confirm the method's superior performance.

Abstract

In recent years, the development of multimodal autoencoders has gained significant attention due to their potential to handle multimodal complex data types and improve model performance. Understanding the stability and robustness of these models is crucial for optimizing their training, architecture, and real-world applicability. This paper presents an analysis of Lipschitz properties in multimodal autoencoders, combining both theoretical insights and empirical validation to enhance the training stability of these models. We begin by deriving the theoretical Lipschitz constants for aggregation methods within the multimodal autoencoder framework. We then introduce a regularized attention-based fusion method, developed based on our theoretical analysis, which demonstrates improved stability and performance during training. Through a series of experiments, we empirically validate our theoretical findings by estimating the Lipschitz constants across multiple trials and fusion strategies. Our results demonstrate that our proposed fusion function not only aligns with theoretical predictions but also outperforms existing strategies in terms of consistency, convergence speed, and accuracy. This work provides a solid theoretical foundation for understanding fusion in multimodal autoencoders and contributes a solution for enhancing their performance.