Understanding Multimodal Contrastive Learning Through Pointwise Mutual Information
Multimodal representation learning to integrate different modalities, such as text, vision, and audio is important for real-world applications. The symmetric InfoNCE loss proposed in CLIP is a key concept in multimodal representation learning. In this work, we provide a theoretical understanding of the symmetric InfoNCE loss through the lens of the pointwise mutual information and show that encoders that achieve the optimal similarity in the pretraining provide a good representation for downstream classification tasks under mild assumptions. Based on our theoretical results, we also propose a new similarity metric for multimodal contrastive learning by utilizing a nonlinear kernel to enrich the capability. To verify the effectiveness of the proposed method, we demonstrate pretraining of multimodal representation models on the Conceptual Caption datasets and evaluate zero-shot classification and linear classification on common benchmark datasets.
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CIFAR-10
CIFAR-100
MNIST
STL-10
Stanford Cars
Food-101
Conceptual Captions
CC12M
