Introduction to the Conditional GAN - A General Framework for Pixel2Pixel Translation

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Duration: 5:14
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5-min ML Paper Challenge
Presenter: https://www.linkedin.com/in/pearl-su-248423a2/

Image-to-Image Translation with Conditional Adversarial Networks
https://arxiv.org/pdf/1611.07004.pdf

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. Indeed, since the
release of the pix2pix software associated with this paper,
a large number of internet users (many of them artists)
have posted their own experiments with our system, further
demonstrating its wide applicability and ease of adoption
without the need for parameter tweaking. 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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Tags:
machine learning
Deep learning
GAN
Computer Vision
conditional gan
CGAN
C-GAN
Style Transfer