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.

翻译：深度生成模型及其条件变体为数据分布通用生成建模提供了强大能力。然而，现有方法仍难以在无标注条件下解决高级条件生成问题，这类问题可支撑图像到图像转换和图像编辑等多种应用。为此，我们提出一个统一的贝叶斯框架，通过在训练过程中引入对潜在变量的推理阶段来应对此类挑战。具体而言，我们设计了变分贝叶斯图像转换网络（VBITN），可以实现多种图像转换与编辑任务。大量实验证明了该方法在无监督图像到图像转换中的有效性，并展示了其在语义编辑和混合域转换方面的新兴高级能力。