Better Together: Leveraging Unpaired Multimodal Data for Stronger
  Unimodal Models
Authors: name: Sharut Gupta, name: Shobhita Sundaram, name: Chenyu Wang, name: Stefanie Jegelka, name: Phillip Isola
Organization: Massachusetts Institute of Technology

Traditional multimodal learners find unified representations for tasks like
visual question answering, but rely heavily on paired datasets. However, an
overlooked yet potentially powerful question is: can one leverage auxiliary
unpaired multimodal data to directly enhance representation learning in a
target modality? We introduce UML: Unpaired Multimodal Learner, a
modality-agnostic training paradigm in which a single model alternately
processes inputs from different modalities while sharing parameters across
them. This design exploits the assumption that different modalities are
projections of a shared underlying reality, allowing the model to benefit from
cross-modal structure without requiring explicit pairs. Theoretically, under
linear data-generating assumptions, we show that unpaired auxiliary data can
yield representations strictly more informative about the data-generating
process than unimodal training. Empirically, we show that using unpaired data
from auxiliary modalities -- such as text, audio, or images -- consistently
improves downstream performance across diverse unimodal targets such as image
and audio. Our project page: https://unpaired-multimodal.github.io/