Unpaired Joint Distribution Modeling via Multi-Scale Image Representations
This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasible couplings. We propose LUD-MSR, a latent-variable probabilistic framework that models the joint distribution via auxiliary representations and optimizes evidence lower bounds using only marginal data. Under mild assumptions, we establish an upper bound on the distribution approximation error. This analysis reveals a trade-off in representation learning between domain consistency and information preservation. To address this trade-off, we introduce a Multi-Scale image Representation (MSR) mapping that exploits structural similarity at coa...
| Authors | Yihang Zou, Hui Zhang, Zuowei Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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