Theoretical refinement of CLIP by utilizing linear structure of optimal similarity

Naoki Yoshida, Satoshi Hayakawa, Yuhta Takida, Toshimitsu Uesaka, Hiromi Wakaki, Yuki Mitsufuji

Keywords: contrastive learning, multimodal representation learning, kernel mean embedding, pointwise mutual information

Abstract:

In this study, we propose an enhancement to the similarity computation mechanism in multi-modal contrastive pretraining frameworks such as CLIP.
Prior theoretical research has demonstrated that the optimal similarity metrics between paired modalities should correspond to the pointwise mutual information (PMI) between the two modalities.
However, the current implementations of CLIP and its variants fail to fully utilize the underlying linear structure of PMI.
We therefore propose KME-CLIP, which leverages this structure through the inner product in a reproducing kernel Hilbert space.
We theoretically prove that our method can approximate PMI with arbitrary accuracy and empirically demonstrate that our approach overall outperforms the standard CLIP formulation across several retrieval and classification tasks.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 11122