Difference between revisions of "Image-to-Image Translation"
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* [http://machinelearningmastery.com/a-gentle-introduction-to-pix2pix-generative-adversarial-network/ A Gentle Introduction to Pix2Pix Generative Adversarial Network | Jason Brownlee - Machine Learning Mastery] | * [http://machinelearningmastery.com/a-gentle-introduction-to-pix2pix-generative-adversarial-network/ A Gentle Introduction to Pix2Pix Generative Adversarial Network | Jason Brownlee - Machine Learning Mastery] | ||
Revision as of 09:08, 19 July 2020
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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
Contents
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
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
pix2pix GAN
TUNIT
- Rethinking the Truly Unsupervised Image-to-Image Translation | K. Baek, Y. Choi, Y. Uh, J. Yoo, and H. Shim - arViv.org
- clovaai/tunit | GitHub
