MM-LeJEPA: Correlated Gaussian Regularization for Multimodal Joint-Embedding Predictive Architectures
LeJEPA derives an isotropic Gaussian embedding target by minimizing, under its task assumptions, a Fisher information criterion that controls the task-averaged squared prediction bias. This enables unimodal self-supervised learning without reconstruction decoders, exponential-moving-average teachers, or contrastive negatives. However, its isotropic task prior does not account for the cross-modal structure of multimodal tasks that read shared content. We introduce MM-LeJEPA by extending LeJEPA's task assumptions to multimodal learning. Specifically, we model multimodal tasks as strength-weighted combinations of components that read independent information from the two modalities and compon...
| Authors | Haowen Bai, Rui Gong$, Meichen Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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