Conditional Deep Convolutional GAN Keras Implementation
Conditional Deep Convolutional GAN Keras Implementation
- 1 minIn this really short post I will show the overall architecture and the results of a Conditional DCGAN. There are several tutorials and explanations for GANs, so the intention with post is just to showcase the results and share the code.
Implementation can be found here
Why is it conditional?
Because we are using an extra piece of class information besides the random noise input. That means we can control the class of the generated image based on a input label.
Architecture
Generator $G(z, c)$
The input for the generator is a noise vector $z \in N(0, 1)$ and a condition vector $c$ which describes the label which we want to generate. With $c$ the $G(z, c)$ model will learn the conditional distribution of the data.
Discriminator $D(X, c)$
The goal of the discriminator is to decide if the input image with the condition vector $c$ came from the original dataset or from $G$.
Results
Generated digits at every epoch:
Linear interpolation results:
Gábor Vecsei
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