Image-to-Image Translation with Conditional Adversarial Nets

Image-to-Image Translation with Conditional Adversarial Nets
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.

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Here we show comprehensive results from each experiment in our paper. Please see the paper for details on these experiments.

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Generative adversarial networks have been vigorously explored in the last two years, and many conditional variants have been proposed. Please see the discussion of related work in our paper. Below we point out two papers that especially influenced this work: the original GAN paper from Goodfellow et al., and the DCGAN framework, from which our code is derived. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Networks NIPS, 2014. [PDF] Alec Radford, Luke Metz, Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks ICLR, 2016. [PDF]

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We thank Richard Zhang and Deepak Pathak for helpful discussions. This work was supported in part by NSF SMA-1514512, NGA NURI, IARPA via Air Force Research Laboratory, Intel Corp, and hardware donations by nVIDIA. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL or the U.S. Government.