# 生成模型¶

## CycleGAN (ICCV’2017)¶

CycleGAN (ICCV'2017)
@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}
}


## Pix2Pix (CVPR’2017)¶

Pix2Pix (CVPR'2017)
@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}
}


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.