Generative Adversarial Networks. A relatively new innovation in the field of deep learning is the Generative Adversarial Network (GAN). GANs use two different networks, one that generates images of what it thinks are shoes (this is called the generator), and another that tries to distinguish between the fake shoes and real shoes from the data set (this is called the discriminator). Think of it as a Cold War between these networks, except they’re trying to outsmart each other, not out-arm each other. Once the discriminator basically can’t tell the difference between the generated images and the real images, the model has converged.


GAN Creating Shoes

Wei, Jerry (2019-07-03). "Generating Shoe Designs with Machine Learning". Medium. Retrieved 2019-11-06.
https://towardsdatascience.com/generating-shoe-designs-with-deep-learning-5dde432a23b8

University of Michigan - Ann Arbor

1. GAN can be used to detect glaucomatous images helping the early diagnosis which is essential to avoid partial or total loss of vision

2. GANs that produce photorealistic images can be used to visualize interior design, industrial design, shoes, bags, and clothing items or items for computer games' scenes.Such networks were reported to be used by Facebook.

3. GANs can reconstruct 3D models of objects from images, and model patterns of motion in video.

4. GANs can be used to age face photographs to show how an individual's appearance might change with age.

5. Relevance feedback on GANs can be used to generate images and replace image search systems.


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Miscellaneous applications

 Bisneto, Tomaz Ribeiro Viana; de Carvalho Filho, Antonio Oseas; Magalhães, Deborah Maria Vieira (February 2020). "Generative adversarial network and texture features applied to automatic glaucoma detection". Applied Soft Computing. 90: 106165. doi:10.1016/j.asoc.2020.106165.

Generative adversarial network and texture features applied to automatic glaucoma detection

Vondrick, Carl; Pirsiavash, Hamed; Torralba, Antonio (2016). "Generating Videos with Scene Dynamics". carlvondrick.com. arXiv:1609.02612. Bibcode:2016arXiv160902612V.

Generating Videos with Scene Dynamics

"3D Generative Adversarial Network". 3dgan.csail.mit.edu.
http://3dgan.csail.mit.edu/
https://youtu.be/mfx7uAkUtCI

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