Reproduce “What Makes Multi-modal Learning Better than Single (Provably)” on OpenResearch

About this paper

Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang

Researchers from Tsinghua University, UT Austin, Fudan University, and Shanghai Qi Zhi Institute developed a theoretical framework proving why multi-modal learning can outperform uni-modal approaches. The study introduced a metric for latent representation quality and demonstrated that combining modalities leads to better generalization by achieving superior latent space representations, validated empirically on emotion recognition and synthetic data.

Key insights

• The superior generalization performance of multi-modal learning is fundamentally tied to its ability to learn higher-quality latent representations.
• Learning with more modalities is theoretically beneficial if the reduction in empirical risk outweighs the potential increase in function class complexity, particularly with sufficient training samples.
• The theoretical framework does not rely on strict assumptions about inter-modal probability distributions or the sufficiency of individual modalities, making it broadly applicable.

Summary by alphaXiv.