Hugging Face Trending Papers June 3, 2026

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

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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.

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RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

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Related stories

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks

MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

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