GAN

Basic idea of GAN


We will control what to generate latter -> Conditional Generation




We need to prepare database which is real pictures.

This is where the term 'adversarial' comes from
You can explain the process in different ways...


Note that we fix discriminator parameters and use **gradient ascent ** to update weight.

Structured Learning



Why Structured Learning Challenging?


Can generator learn by itself?





Answer is "The most probility is noise!", how to solve it ?




Can discriminator generate?





Discriminator training needs some negative examples.







Use generator to slove argmax


Conditional Generation by GAN




c:text, x:image pair








Unsupervised Conditional Generation












B

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