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

• URL: http://arxiv.org/abs/2305.13872v1
• Date: Tue, 23 May 2023 09:47:23 GMT
• Title: Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
• Authors: Yuxiao Li, Santiago Mazuelas, Yuan Shen
• Abstract summary: We present a unified Bayesian framework for advanced conditional generative problems. We propose a variational Bayesian image translation network (VBITN) that enables multiple image translation and editing tasks.
• Score: 29.827191184889898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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