Unsupervised image-to-image translation intends to learn a mapping of an
image in a given domain to an analogous image in a different domain, without
explicit supervision of the mapping. Few-shot unsupervised image-to-image
translation further attempts to generalize the model to an unseen domain by
leveraging example images of the unseen domain provided at inference time.
While remarkably successful, existing few-shot image-to-image translation
models find it difficult to preserve the structure of the input image while
emulating the appearance of the unseen domain, which we refer to as the content
loss problem. This is particularly severe when the poses of the objects in the
input and example images are very different. To address the issue, we propose a
new few-shot image translation model, COCO-FUNIT, which computes the style
embedding of the example images conditioned on the input image and a new module
called the constant style bias. Through extensive experimental validations with
comparison to the state-of-the-art, our model shows effectiveness in addressing
the content loss problem. For code and pretrained models, please check out
this https URL .

提出了一种名为 COCO-FUNIT 的 few-shot 无监督图像翻译模型，通过引入样式嵌入和常数样式偏置模块依据输入图像和示例图像，有效解决图像翻译中的内容丢失问题。