18 Impressive Applications of Generative Adversarial Networks (GANs)
Last Updated on July 12, 2019
A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling.
Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating new photographs that are similar but specifically different from a dataset of existing photographs.
A GAN is a generative model that is trained using two neural network models. One model is called the “generator” or “generative network” model that learns to generate new plausible samples. The other model is called the “discriminator” or “discriminative network” and learns to differentiate generated examples from real examples.
The two models are set up in a contest or a game (in a game theory sense) where the generator model seeks to fool the discriminator model, and the discriminator is provided with both examples of real and generated samples.
After training, the generative model can then be used to create new plausible samples on demand.
GANs have very specific use cases and it can be difficult to understand these use cases when getting started.
In this post, we will review a large number of interesting applications of GANs
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