UNIT(Unsupervised Image-to-Image Translation Networks),由NVIDIA-Lab在NIPS 2017年提出,该文章首次提Image-Image Translation这个概念,将计算机视觉和计算机图形学的许多任务总结进去,分为一对多和多对一的两类转换任务,包括CV里的边缘检测,图像分割,语义标签以及CG里的mapping labels or sparse user inputs to realist...
UNIT UNIT(Unsupervised Image-to-Image Translation Networks),由NVIDIA-Lab在NIPS 2017年提出,该文章首次提Image-Image Translation这个概念,将计算机视觉和计算机图形学的许多任务总结进去,分为一对多和多对一的两类转换任务,包括CV里的边缘检测,图像分割,语义标签以及CG里的mapping labels or sparse user inputs to ...
从最开始的有监督的image translation 到之后的无监督的image to image translation再到后来的Multi-modal unsupervised image to image translation. 这个问题的研究逐渐深入,这篇文章主要介绍3篇Multi-modal unsupervised image to image translation: MUNIT, Disentangled Representations, CD-GAN 以及一篇与之相关的工作U...
image_save_iterations: 2500 # How often do you want to save output images during training image_display_iterations: 100 display: 1 # How often do you want to log the training stats snapshot_prefix: ../outputs/unit/celeba/blondhair/blondhair # Where do you want to save the outputs hyper...
FUNIT: Few-Shot Unsupervised Image-to-Image Translation https:// 作者:陈扬 [toc] 简介 无监督的图像到图像转换方法学习利用图像的非结构化(UNlabel)数据集将给定类中的图像映射到不同类中的类似图像。在ICCV2019上,NVIDIA-Lab发表了Image-to-image最新的研究成果,基于少量类别学习的FUNIT.笔者在CVPR2020的投...
Ming-Yu Liu, Thomas Breuel, Jan Kautz, "Unsupervised Image-to-Image Translation Networks" NIPS 2017 Spotlight, arXiv:1703.00848 2017 Two Minute Paper Summary (We thank the Two Minute Papers channel for summarizing our work.) The Shared Latent Space Assumption Result Videos More image results are...
1. MUNIT：NVIDIA实验室的成果，基于共享的潜在空间，改进了Unsupervised image-to-image translation的框架。它的核心思想是将图像转换视为两个域的联合分布推导，通过共享编码器和生成器的底层结构来保证图像源于同一潜在代码。网络结构包括两个VAE-GAN，损失函数结合了VAE的KL散度和GAN的对抗损失，以及循环...
@article{yang2023gp,title={GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation},author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change},journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},year={2023},publisher={IEEE}} ...
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 ...
Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a ...