Image-to-Image Translation with Conditional Adversarial Networks

Speaker(s): Andrew Awad
Topic: Image-to-Image Translation with Conditional Adversarial Networks

Andrew presented on a CVPR 2017 paper by Isola et al. This paper aimed to 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. The networks in question were CGANs, proposed earlier by Mirza et al. Isola et al. also proposed the PatchGAN discriminator.

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Supplemental Resources

Paper(s):
Image-to-Image Translation with Conditional Adversarial Networks
CGAN

Other:
PatchGAN
pix2pix GitHub Page
Medium article on pix2pix