The paper introduces a unified formulation for representation learning with task and constraint components, arguing for mutual benefits between causal and traditional approaches and showing via experiments that causal constraint effectiveness depends on paired tasks.
Object-centric decoders and additive decoder models can both be viewed through this functional- constraint perspective
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A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
The paper introduces a unified formulation for representation learning with task and constraint components, arguing for mutual benefits between causal and traditional approaches and showing via experiments that causal constraint effectiveness depends on paired tasks.