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Generative Adversarial Networks
One of the coolest uses of GANs is doing arithmetic on faces in the generator's
vector Z. In other words, if we stay in the space of synthetic forged images, it is
possible to see things like this: [smiling woman] - [neutral woman] + [neutral man]
= [smiling man], or like this: [man with glasses] - [man without glasses] + [woman
without glasses] = [woman with glasses]. This was shown in the paper Unsupervised
Representation Learning with Deep Convolutional Generative Adversarial Networks by
Alec Radford and his colleagues in 2015. All images in this work are generated by
a version of GAN. They are NOT REAL. The full paper is available here: http://
arxiv.org/abs/1511.06434. Following are some examples from the paper. The
authors also share their code in this GitHub repo: https://github.com/Newmu/
dcgan_code:
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