Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Paper summary _Objective:_ Image-to-image translation to perform visual attribute transfer using unpaired images. _Dataset:_ [Cityscapes](https://www.cityscapes-dataset.com/), [CMP Facade](http://cmp.felk.cvut.cz/%7Etylecr1/facade/), [UT Zappos50k](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/) and [ImageNet](http://www.image-net.org/). _Code:_ [CycleGAN](https://github.com/junyanz/CycleGAN) ## Inner-workings: Basically two GANs for each domain with their respective Generator and Discriminator plus two additional losses (called consistency losses) to make sure that translating to the other domain then back yields an image that is still realistic. [![screen shot 2017-06-02 at 10 24 45 am](https://cloud.githubusercontent.com/assets/17261080/26717449/bcd8a9cc-477d-11e7-9137-fd277a0ec04f.png)](https://cloud.githubusercontent.com/assets/17261080/26717449/bcd8a9cc-477d-11e7-9137-fd277a0ec04f.png) For the consistency los they use a pixel-wise L1 norm: [![screen shot 2017-06-02 at 10 31 22 am](https://cloud.githubusercontent.com/assets/17261080/26717733/bc088cdc-477e-11e7-96af-2defa06a1660.png)](https://cloud.githubusercontent.com/assets/17261080/26717733/bc088cdc-477e-11e7-96af-2defa06a1660.png) ## Architecture: Based on [Perceptual losses for real-time style transfer and super-resolution](https://arxiv.org/pdf/1603.08155.pdf), code available [here](https://github.com/jcjohnson/fast-neural-style). Training seems to employ several tricks and then even use a batch of 1. ## Results: Very impressive and the really key point is that you don't need paired images which makes this trainable on any domain with the same representation behind. [![screen shot 2017-06-02 at 10 26 29 am](https://cloud.githubusercontent.com/assets/17261080/26717502/f6d1fb7e-477d-11e7-8174-7bdd621cf1b6.png)](https://cloud.githubusercontent.com/assets/17261080/26717502/f6d1fb7e-477d-11e7-8174-7bdd621cf1b6.png)
arxiv.org
scholar.google.com
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp


Summary by Léo Paillier 3 months ago
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