On the Generative Utility of Cyclic Conditionals
Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

TL;DR
This paper investigates modeling joint distributions using cyclic conditional models, establishing theoretical criteria for their compatibility and determinacy, and introduces CyGen, a novel framework that improves data fitting by leveraging only these two conditionals.
Contribution
It provides a comprehensive theory for the compatibility and determinacy of cyclic conditionals and proposes CyGen, a new generative framework that operates solely on these conditionals.
Findings
CyGen better fits data compared to traditional models.
Theoretical criteria for compatibility and determinacy are established.
Experimental results demonstrate improved feature representation.
Abstract
We study whether and how can we model a joint distribution using two conditional models and that form a cycle. This is motivated by the observation that deep generative models, in addition to a likelihood model , often also use an inference model for extracting representation, but they rely on a usually uninformative prior distribution to define a joint distribution, which may render problems like posterior collapse and manifold mismatch. To explore the possibility to model a joint distribution using only and , we study their compatibility and determinacy, corresponding to the existence and uniqueness of a joint distribution whose conditional distributions coincide with them. We develop a general theory for operable equivalence criteria for compatibility, and sufficient conditions for determinacy. Based on the theory,…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Generative Adversarial Networks and Image Synthesis · Explainable Artificial Intelligence (XAI)
