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arXiv · 2305.13872

Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables

Yuxiao Li·Santiago Mazuelas·Yuan Shen
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Abstract

Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications like image-to-image translation and image editing. We present a unified Bayesian framework for such problems, which introduces an inference stage on latent variables within the learning process. In particular, we propose a variational Bayesian image translation network (VBITN) that enables multiple image translation and editing tasks. Comprehensive experiments show the effectiveness of our method on unsupervised image-to-image translation, and demonstrate the novel advanced capabilities for semantic editing and mixed domain translation.

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Yuxiao Li, Santiago Mazuelas, Yuan Shen. 2023-05-23. Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables. https://doi.org/10.1109/icassp43922.2022.9746364

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