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Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics across disparate domains. Existing methods assume shared features across data tables to enable knowledge transfer between domains, which is unrealistic in practice. \mds{This paper introduces generalized context learning to remove the requirement of shared features across domains. The generalized context captured by transformer projection weights for $key$, $value$, and $query$ provides rule-based generalization rather than the domain-specific conte

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