GAN feedback loop Mario Klingemann


Mario writes:

This new version uses an additional model that allows for a 1024×1024 output and adds more details like skin texture, eyebrows and eyelashes. There are still several technical issues that I need to find a solution for, like for example the repetitive patterns that appear in low-entropy areas.

Youtube Channel from Mario Klingemann

Also check out the great talk about the topic.

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning, fully supervised learning, and reinforcement learning.The core idea of a GAN is based on the "indirect" training through the discriminator, which itself is also being updated dynamically. This basically means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner.
Definition from Wikipedia – Generative adversarial network

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