README.md

CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Introduction

@inproceedings{zhu2017unpaired,
  title={Unpaired image-to-image translation using cycle-consistent adversarial networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2223--2232},
  year={2017}
}

Results and Models

We use FID and IS metrics to evaluate the generation performance of CycleGAN.

FID evaluation:

Dataset	facades	facades-id0	summer2winter	summer2winter-id0	winter2summer	winter2summer-id0	horse2zebra	horse2zebra-id0	zebra2horse	zebra2horse-id0	average
official	123.626	119.726	77.342	76.773	72.631	74.239	62.111	77.202	138.646	137.050	95.935
ours	118.297	126.316	76.959	76.018	72.803	73.498	63.810	71.675	139.279	132.369	95.102

IS evaluation:

Dataset	facades	facades-id0	summer2winter	summer2winter-id0	winter2summer	winter2summer-id0	horse2zebra	horse2zebra-id0	zebra2horse	zebra2horse-id0	average
official	1.638	1.697	2.762	2.750	3.293	3.110	1.375	1.584	3.186	3.047	2.444
ours	1.584	1.957	2.768	2.735	3.069	3.130	1.430	1.542	3.093	2.958	2.427

Model and log downloads:

Dataset	facades	facades-id0	summer2winter	summer2winter-id0	horse2zebra	horse2zebra-id0
download	model | log	model | log	model | log	model | log	model | log	model | log

Note: With a larger identity loss, the image-to-image translation becomes more conservative, which makes less changes. The original authors did not say what is the best weight for identity loss. Thus, in addition to the default setting, we also set the weight of identity loss to 0 (denoting id0) to make a more comprehensive comparison.