Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

arXiv:2608.07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing approaches do not operate as full-stack wearable processors, i.e., they do not simultaneously address task-specific classification performance, disentangled and interpretable…

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Source: cs.AI updates on arXiv.org

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