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Sunday, February 24, 2019

Advances in GANS

To me one of the most interesting examples of neural neworks, a kind of mixture between nets and game simulation.    We did do something similar by generating process examples to test Monte Carlo simulation.   Have never implemented one, but worth knowing about.

Advances in Generative Adversarial Networks

A summary of the latest advances in Generative Adversarial Networks
Written by Bharath Raj with feedback from Rotem Alaluf

Generative Adversarial Networks are a powerful class of neural networks with remarkable applications. They essentially consist of a system of two neural networks — the Generator and the Discriminator — dueling each other.

Given a set of target samples, the Generator tries to produce samples that can fool the Discriminator into believing they are real. The Discriminator tries to resolve real (target) samples from fake (generated) samples. Using this iterative training approach, we eventually end up with a Generator that is really good at generating samples similar to the target samples.

GANs have a plethora of applications, as they can learn to mimic data distributions of almost any kind. Popularly, GANs are used for removing artefacts, super resolution, pose transfer, and literally any kind of image translation, as shown below:  ....  "

And also, CycleGans, a variation that has been used to generate Art.

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