Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
Toshimitsu Uesaka Taiji Suzuki Yuhta Takida Chieh-Hsin Lai Naoki Murata Yuki Mitsufuji
In typical multimodal contrastive learning, such as CLIP, encoders produce one
point in the latent representation space for each input. However, one-point representation
has difficulty in capturing the relationship and the similarity structure of a
huge amount of instances in the real world. For richer classes of the similarity, we
propose the use of weighted point sets, namely, sets of pairs of weight and vector,
as representations of instances. In this work, we theoretically show the benefit
of our proposed method through a new understanding of the contrastive loss of
CLIP, which we call symmetric InfoNCE. We clarify that the optimal similarity
that minimizes symmetric InfoNCE is the pointwise mutual information, and show
an upper bound of excess risk on downstream classification tasks of representations
that achieve the optimal similarity. In addition, we show that our proposed
similarity based on weighted point sets consistently achieves the optimal similarity.
To verify the effectiveness of our proposed method, we demonstrate pretraining of
text-image representation models and classification tasks on common benchmarks.
contrastive learning, representation learning, multimodal representation learning, theoretical analysis, InfoNCE, pointwise mutual information
We propose a new multimodal representation learning method and theoretically show benefits of our method.