Image-to-Image Translation with Conditional Adversarial Networks
Alexei A. Efros
arXiv e-Print archive - 2016 via Local arXiv
First published: 2016/11/21 (5 years ago) 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.