RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data. However, while multimodal disentanglement is a compelling paradigm, existing methods are largely confined to the two-modality regime due to its inherent scalability bottleneck. To address this, we propose RePercENT, a self-supervised framework designed to surpass these limitations and unlocks scalable pairwis...
| Authors | Vasiliki Rizou, Pascal Frossard, Dorina Thanou |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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