Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shift
It addresses the challenge of building data-efficient and robust AI systems for dynamic real-world environments, but appears incremental as it builds on established co-training principles.
This paper tackles the problem of improving model generalization under data scarcity and distribution shifts by proposing a multimodal co-training framework, deriving theoretical conditions and a novel generalization bound that quantifies advantages from unlabeled data, inter-view agreement, and conditional independence.
This paper explores a multimodal co-training framework designed to enhance model generalization in situations where labeled data is limited and distribution shifts occur. We thoroughly examine the theoretical foundations of this framework, deriving conditions under which the use of unlabeled data and the promotion of agreement between classifiers for different modalities lead to significant improvements in generalization. We also present a convergence analysis that confirms the effectiveness of iterative co-training in reducing classification errors. In addition, we establish a novel generalization bound that, for the first time in a multimodal co-training context, decomposes and quantifies the distinct advantages gained from leveraging unlabeled multimodal data, promoting inter-view agreement, and maintaining conditional view independence. Our findings highlight the practical benefits of multimodal co-training as a structured approach to developing data-efficient and robust AI systems that can effectively generalize in dynamic, real-world environments. The theoretical foundations are examined in dialogue with, and in advance of, established co-training principles.