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Unsupervised Learning: Difference between revisions
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==GANs== | ==GANs== | ||
{{main | Generative adversarial network}} | {{main | Generative adversarial network}} | ||
===Wasserstein GAN=== | |||
[https://arxiv.org/abs/1701.07875 Paper]<br> | |||
The main idea is to ensure the that discriminator is lipschitz continuous and to limit the lipschitz constant (i.e. the derivative) of the discriminator.<br> | |||
If the correct answer is 1.0 and the generator produces 1.0001, we don't want the discriminator to give us a very high loss.<br> | |||