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.
Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324
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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.