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.
When Is Unsupervised Disentanglement Possible? , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
citing papers explorer
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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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Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.