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Unsupervised Learning: Difference between revisions
→Wasserstein GAN
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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> | 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> | 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> | ||
====Earth mover's distance==== | |||
{{main | wikipedia:earth mover's distance}} | |||
The minimum cost of converting one pile of dirt to another.<br> | |||
Where cost is the cost of moving (amount * distance)<br> | |||
Given a set <math>P</math> with m clusters and a set <math>Q</math> with n clusters:<br> | |||
... | |||
<math>EMD(P, Q) = \frac{\sum_{i=1}^{m}\sum_{j=1}^{n}f_{i,j}}{\sum_{i=1}^{m}\sum_{j=1}^{n}f_{i,j}}</math><br> | |||
;Notes | |||
* Also known as Wasserstein metric | |||