Unpaired multimodal image-to-image translation is a task of translating a given image in a source domain into diverse images in the target domain, overcoming the limitation of one-to-one mapping. Existing multimodal translation models are mainly based on the disentangled representations with an image reconstruction loss. We propose two approaches to improve multimodal translation quality. First, we use a content representation from the source domain conditioned on a style representation from the target domain. Second, rather than using a typical image reconstruction loss, we design MILO (Mutual Information LOss), a new stochastically-defined loss function based on information theory. This loss function directly reflects the interpretation of latent variables as a random variable. We show that our proposed model Mutual Information with StOchastic Style Representation(MISO) achieves state-of-the-art performance through extensive experiments on various real-world datasets.

本文提出两种方法来提高多模态翻译质量，其一是通过来自源领域的内容表示以及目标域的风格表示的条件表示，其二是采用MILO，一种基于信息论的新型随机定义的损失函数。我们的模型MISO在各种真实数据集上广泛实验，取得了最优性能。

多模态图像到图像转换的随机风格表示与相互信息损失