RePercENT introduces a plug-and-play self-supervised framework for scalable pairwise disentangled representation learning across more than two modalities using pre-extracted embeddings and a joint optimization objective with theoretical optimality guarantees.
Hanxiao Liu, Zihang Dai, David So, and Quoc V Le
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RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
RePercENT introduces a plug-and-play self-supervised framework for scalable pairwise disentangled representation learning across more than two modalities using pre-extracted embeddings and a joint optimization objective with theoretical optimality guarantees.