Image-to-Image Translation

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• Generated Image
• [Image-to-Image Translation | Yongfu Hao
• 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.

Approaches:

• Paired
• Unparied

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

• Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks | J. Zhu, T. Park, P. Isola, and A. Efros - Semantic Scholar

• CycleGAN: Learning to Translate Images (Without Paired Training Data) | Sarah Wolf - Towards Data Science

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

• A Gentle Introduction to Pix2Pix Generative Adversarial Network | Jason Brownlee - Machine Learning Mastery

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