pix2pix: first GAN-based style transfer model for paired data

Released in: Image-to-Image Translation with Conditional Adversarial Networks



The authors investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. The paper demonstrates that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we had no longer hand-engineered our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.


Year Released

Key Links & Stats

CycleGAN and pix2pix in PyTorch


Image-to-Image Translation with Conditional Adversarial Networks

@INPROCEEDINGS{8100115, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A.}, booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, title={Image-to-Image Translation with Conditional Adversarial Networks}, year={2017}, volume={}, number={}, pages={5967-5976}, doi={10.1109/CVPR.2017.632}}

ML Tasks

  1. General
  2. Domain Adaptation
  3. Style Transfer

ML Platform

  1. Pytorch


  1. General
  2. Still Image


  1. General

CG Platform

  1. Not Applicable

Related organizations

UC Berkeley