Note

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Generation Models

CycleGAN (ICCV’2017)

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

Abstract

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G:X→Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F:Y→X and introduce a cycle consistency loss to push F(G(X))≈X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.

Results and models

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

Method	FID	IS	Download
official facades	123.626	1.638	-
ours facades	118.297	1.584	model | log
official facades-id0	119.726	1.697	-
ours facades-id0	126.316	1.957	model | log
official summer2winter	77.342	2.762	-
ours summer2winter	76.959	2.768	model | log
official winter2summer	72.631	3.293	-
ours winter2summer	72.803	3.069	model | log
official summer2winter-id0	76.773	2.750	-
ours summer2winter-id0	76.018	2.735	model | log
official winter2summer-id0	74.239	3.110	-
ours winter2summer-id0	73.498	3.130	model | log
official horse2zebra	62.111	1.375	-
ours horse2zebra	63.810	1.430	model | log
official horse2zebra-id0	77.202	1.584	-
ours horse2zebra-id0	71.675	1.542	model | log
official horse2zebra	138.646	3.186	-
ours zebra2horse	139.279	3.093	model | log
official horse2zebra-id0	137.050	3.047	-
ours zebra2horse-id0	132.369	2.958	model | log
official average	95.935	2.444	-
ours average	95.102	2.427	-

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.

Citation

@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)

Image-to-Image Translation with Conditional Adversarial Networks

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. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

Results and models

We use FID and IS metrics to evaluate the generation performance of pix2pix.

Method	FID	IS	Download
official facades	119.135	1.650	-
ours facades	127.792	1.745	model | log
official maps-a2b	149.731	2.529	-
ours maps-a2b	118.552	2.689	model | log
official maps-b2a	102.072	3.552	-
ours maps-b2a	92.798	3.473	model | log
official edges2shoes	75.774	2.766	-
ours edges2shoes	85.413	2.747	model | log
official average	111.678	2.624	-
ours average	106.139	2.664	-

Note: 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.

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}
}