Difference between revisions of "Image-to-Image Translation"

From
Jump to: navigation, search
(pix2pix)
Line 11: Line 11:
 
* [[http://towardsdatascience.com/image-to-image-translation-69c10c18f6ff Image-to-Image Translation | Yongfu Hao]
 
* [[http://towardsdatascience.com/image-to-image-translation-69c10c18f6ff Image-to-Image Translation | Yongfu Hao]
 
* [[Generative Adversarial Network (GAN)]]
 
* [[Generative Adversarial Network (GAN)]]
 +
 +
Image-to-image translation is the controlled conversion of a given source image to a target image. Examples might be the conversion of day image to night image, or black and white photographs to color photographs.
  
 
<youtube>sIkUzmgUaxc</youtube>
 
<youtube>sIkUzmgUaxc</youtube>

Revision as of 08:32, 19 July 2020

YouTube search... ...Google search

Image-to-image translation is the controlled conversion of a given source image to a target image. Examples might be the conversion of day image to night image, or black and white photographs to color photographs.

Approaches:

  • Paired
  • Unparied

0*P-46iNsLcF2edVfn.png

StarGAN

Existing image to image translation approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. StarGAN is a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Image-to-Image Translation | Yongfu Hao

0*S7N84-uT_6zqrhxl.png


CycleGAN

An approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples Image-to-Image Translation | Yongfu Hao

0*KXiC6nIcowYS5GtA.png


pix2pix

TUNIT