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Poster
What Makes Multi-Modal Learning Better than Single (Provably)
Yu Huang · Chenzhuang Du · Zihui Xue · Xuanyao Chen · Hang Zhao · Longbo Huang

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @
in Poster Session 4 »

The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is aggregated. Recently, joining the success of deep learning, there is an influential line of work on deep multi-modal learning, which has remarkable empirical results on various applications. However, theoretical justifications in this field are notably lacking. Can multi-modal learning provably perform better than uni-modal?In this paper, we answer this question under a most popular multi-modal fusion framework, which firstly encodes features from different modalities into a common latent space and seamlessly maps the latent representations into the task space. We prove that learning with multiple modalities achieves a smaller population risk than only using its subset of modalities. The main intuition is that the former has a more accurate estimate of the latent space representation. To the best of our knowledge, this is the first theoretical treatment to capture important qualitative phenomena observed in real multi-modal applications from the generalization perspective. Combining with experiment results, we show that multi-modal learning does possess an appealing formal guarantee.

Keywords: Theory · Deep Learning

Author Information

Yu Huang (Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University)
Chenzhuang Du (Tsinghua University)
Zihui Xue (University of Texas, Austin)
Xuanyao Chen (Fudan University)
Hang Zhao
Longbo Huang (IIIS, Tsinghua Univeristy)

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