  Recent contrastive learning methods have shown to be effective in various
tasks, learning generalizable representations invariant to data augmentation
thereby leading to state of the art performances. Regarding the multifaceted
nature of large unlabeled data used in self-supervised learning while majority
of real-word downstream tasks use single format of data, a multimodal framework
that can train single modality to learn diverse perspectives from other
modalities is an important challenge. In this paper, we propose TriCL
(Triangular Contrastive Learning), a universal framework for trimodal
contrastive learning. TriCL takes advantage of Triangular Area Loss, a novel
intermodal contrastive loss that learns the angular geometry of the embedding
space through simultaneously contrasting the area of positive and negative
triplets. Systematic observation on embedding space in terms of alignment and
uniformity showed that Triangular Area Loss can address the line-collapsing
problem by discriminating modalities by angle. Our experimental results also
demonstrate the outperformance of TriCL on downstream task of molecular
property prediction which implies that the advantages of the embedding space
indeed benefits the performance on downstream tasks.
