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Pix2Pix (CVPR'2017)

Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks

Task: Image2Image

Abstract

We 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. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

Results and Models

Results from Pix2Pix trained by mmagic
We use `FID` and `IS` metrics to evaluate the generation performance of pix2pix.1
Model Dataset FID IS Download
Ours facades 124.9773 1.620 model | log2
Ours aerial2maps 122.5856 3.137 model
Ours maps2aerial 88.4635 3.310 model
Ours edges2shoes 84.3750 2.815 model

FID comparison with official:

Dataset facades aerial2maps maps2aerial edges2shoes average
official 119.135 149.731 102.072 75.774 111.678
ours 124.9773 122.5856 88.4635 84.3750 105.1003

IS comparison with official:

Dataset facades aerial2maps maps2aerial edges2shoes average
official 1.650 2.529 3.552 2.766 2.624
ours 1.620 3.137 3.310 2.815 2.7205

Note:

  1. we strictly follow the paper setting in Section 3.3: "At inference time, we run the generator net in exactly the same manner as during the training phase. This differs from the usual protocol in that we apply dropout at test time, and we apply batch normalization using the statistics of the test batch, rather than aggregated statistics of the training batch." (i.e., use model.train() mode), thus may lead to slightly different inference results every time.
  2. This is the training log before refactoring. Updated logs will be released soon.

Citation

@inproceedings{isola2017image,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1125--1134},
  year={2017},
  url={https://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html},
}