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(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...
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...
Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences ...
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...
In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g....
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 ...