We introduce a framework for generating highly multimodal datasets with explicitly calculable mutual information between modalities. This enables the construction of benchmark datasets that provide a novel testbed for systematic studies of mutual information estimators and multimodal self-supervised learning techniques. Our framework constructs realistic datasets with known mutual information using a flow-based generative model and a structured causal framework for generating correlated latent variables.

翻译：我们提出了一个框架，用于生成高度多模态的数据集，其中模态间的互信息可显式计算。这使得构建基准数据集成为可能，为互信息估计器和多模态自监督学习技术的系统性研究提供了新颖的测试平台。我们的框架利用基于流的生成模型和用于生成相关潜变量的结构化因果框架，构建了具有已知互信息的真实数据集。