SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.
Communication-efficient and robust multi-modal federated learning via latent-space consensus.IEEE Wireless Communications Letters, 15:2298–2302
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SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.