Image-to-Image Translation(12IT)aims to learn the mapping between images of different domains.Image translation is a hot topic in computer vision since 12IT models can be applied in various fields,including image style transfer,semantic segmentation,and image super-resolution.With the booming development of deep learning,numerous effective I2IT models have emerged in recent years.Among them,unsupervised models based on disentangled content-style representation are essential methods.In this paper,we first review the development of such models in terms of content and style representation.Then,we summarize the common datasets and metrics used in I2IT tasks and compare the results of various models.Finally,we assess and forecast future trends in I2IT development.
GANimage-to-image translationdisentangled latent space
维普
万方数据
