Unsupervised image translation

By Rómer Rosales, Kannan Achan, Brendan J. Frey. 2003

73 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Multimodal Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ming-Hsuan Yang
2. Luc Van Gool
3. Jitendra Malik
4. Trevor Darrell
5. Dacheng Tao
6. Alexei A. Efros
7. Xiaogang Wang
8. Andrew Zisserman

In Generative Models

1. Jun-Yan Zhu
2. Alexei A. Efros
3. Taesung Park
4. Phillip Isola
5. Eli Shechtman
6. Richard Zhang
7. William T. Freeman
8. Toby P. Breckon

In Multimodal Learning

1. Alexei A. Efros
2. Phillip Isola
3. Jun-Yan Zhu
4. Wojciech Matusik
5. William T. Freeman
6. Taesung Park
7. Kristen Grauman
8. Ruohan Gao

Citations in the corpus

• Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks (2017, methodology): “[37] propose a Bayesian framework that includes a prior based on a patchbased Markov random field computed from a source image, and a likelihood term obtained from multiple style images.”
• Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer (2018, background): “While the notion of transforming images from one domain to the other is not new [90, 51, 63, 7, 70], we utilize image style transfer using generative adversarial networks, as proposed in [90], to reduce the discrepancy between our source domain (real-world data) and our target domain (synthetic data”
• CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs (2011, background): “[9] relaxed this assumption by framing the problem as a large inference problem, where both the position and appearance of the patches are inferred from a pair of images without correspondence.”