In multimodal unsupervised image-to-image translation tasks, the goal is to
translate an image from the source domain to many images in the target domain.
We present a simple method that produces higher quality images than current
state-of-the-art while maintaining the same amount of multimodal diversity.
Previous methods follow the unconditional approach of trying to map the latent
code directly to a full-size image. This leads to complicated network
architectures with several introduced hyperparameters to tune. By treating the
latent code as a modifier of the convolutional filters, we produce multimodal
output while maintaining the traditional Generative Adversarial Network (GAN)
loss and without additional hyperparameters. The only tuning required by our
method controls the tradeoff between variability and quality of generated
images. Furthermore, we achieve disentanglement between source domain content
and target domain style for free as a by-product of our formulation. We perform
qualitative and quantitative experiments showing the advantages of our method
compared with the state-of-the art on multiple benchmark image-to-image
translation datasets.

我们提出了一种简单的方法，它能够在不增加超参数的情况下产生高质量的多模态输出，将潜在代码视为卷积滤波器的修改器，从而使源域内容与目标域风格解耦。

多模态无监督图像到图像转换的潜在过滤器缩放

Latent Filter Scaling for Multimodal Unsupervised Image-to-Image  Translation

Unsupervised image-to-image translation is an important and challenging
problem in computer vision. Given an image in the source domain, the goal is to
learn the conditional distribution of corresponding images in the target
domain, without seeing any pairs of corresponding images. While this
conditional distribution is inherently multimodal, existing approaches make an
overly simplified assumption, modeling it as a deterministic one-to-one
mapping. As a result, they fail to generate diverse outputs from a given source
domain image. To address this limitation, we propose a Multimodal Unsupervised
Image-to-image Translation (MUNIT) framework. We assume that the image
representation can be decomposed into a content code that is domain-invariant,
and a style code that captures domain-specific properties. To translate an
image to another domain, we recombine its content code with a random style code
sampled from the style space of the target domain. We analyze the proposed
framework and establish several theoretical results. Extensive experiments with
comparisons to the state-of-the-art approaches further demonstrates the
advantage of the proposed framework. Moreover, our framework allows users to
control the style of translation outputs by providing an example style image.
Code and pretrained models are available at this https URL

本研究提出了一种多模态无监督图像到图像的翻译框架 (MUNIT)，该框架可以将图像表示分解为内容代码和风格代码，从而能够生成来自给定源域图像的多样性输出，并能够通过提供示例样式图像来控制翻译输出的风格。该框架在对比最先进的方法的基础上，通过大量实验证明了其优势。