论文标题
MCMI：具有共同信息约束的多周期图像翻译
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
论文作者
论文摘要
我们提供了一个基于信息的基于信息的框架，用于无监督的图像到图像翻译。我们的MCMI方法将单个周期图像翻译模型视为模块，可以在多周期翻译设置中经常使用，其中转换过程受输入和输出图像之间的相互信息约束所界定。提出的相互信息约束可以通过优化在图像翻译过程中无法满足Markov属性的翻译功能来改善跨域映射。我们表明，与最先进的图像翻译方法相比，接受MCMI训练的模型会产生更高质量的图像，并学习更多与语义相关的映射。 MCMI框架可以应用于具有最小修改的现有未配对的图像到图像翻译模型。定性实验和感知研究表明了使用多种主链模型和各种图像数据集的图像质量的改进和通用性。
We present a mutual information-based framework for unsupervised image-to-image translation. Our MCMI approach treats single-cycle image translation models as modules that can be used recurrently in a multi-cycle translation setting where the translation process is bounded by mutual information constraints between the input and output images. The proposed mutual information constraints can improve cross-domain mappings by optimizing out translation functions that fail to satisfy the Markov property during image translations. We show that models trained with MCMI produce higher quality images and learn more semantically-relevant mappings compared to state-of-the-art image translation methods. The MCMI framework can be applied to existing unpaired image-to-image translation models with minimum modifications. Qualitative experiments and a perceptual study demonstrate the image quality improvements and generality of our approach using several backbone models and a variety of image datasets.
