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Generative adversarial network: Difference between revisions
Generative adversarial network (view source)
Revision as of 16:23, 24 January 2020
, 24 January 2020→Basis Structure
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Goal: Learn to generate examples from the same distribution as your training set. | Goal: Learn to generate examples from the same distribution as your training set. | ||
== | ==Structure== | ||
GANs consist of a generator and a discriminator. | GANs consist of a generator and a discriminator, both of which are usually CNNs. | ||
<pre> | <pre> | ||
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Update Generator | Update Generator | ||
</pre> | </pre> | ||
===Generator=== | |||
Two popular types of CNNs used in GANs are Resnets and UNets.<br> | |||
In both cases, we have convolutional blocks which consist of a conv2d layer, a batch norm, and an activation (typically Relu or leakyrelu). | |||
===Discriminator=== | |||
A popular discriminator is the PatchGAN discriminator.<br> | |||
These are typically several convolutional blocks stacked together. | |||
Each convolutional layer in the conv block typically has a kernel size of (3x3) or (4x4) and a stride of 1-2. | |||
==Variations== | ==Variations== | ||