Multimodal Datasets with Controllable Mutual Information
Generating highly multimodal datasets with explicitly calculable mutual information between modalities.
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.
For more information, please see our paper.
Raheem Hashmani
PhD student at UW–Madison
UW–Madison • DSI • AMS • CERN
Ph.D. student interested on novel applications of deep learning in high energy physics and astrophysics.