Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
u gelgen, J., Stimper, V., Sch \
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A representation learning approach for multi-source domain adaptation achieves identifiability by partitioning the label's Markov blanket into parents, children, and spouses.
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Content-Style Identification via Differential Independence
Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
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A General Representation-Based Approach to Multi-Source Domain Adaptation
A representation learning approach for multi-source domain adaptation achieves identifiability by partitioning the label's Markov blanket into parents, children, and spouses.