Course Content
Image Synthesis Through Generative Networks
Image Synthesis Through Generative Networks
Conditional GAN
Conditional GANs (cGANs) extend the GAN framework by conditioning both the generator and discriminator on additional information. This auxiliary data can be anything from class labels, images, text, or any other contextual information the model can use to guide the generation process. By doing so, cGANs enable a more controlled and targeted generation of data.
Generator
The generator takes two inputs: a random noise vector and the conditioning information . These inputs are concatenated and fed into the generator network, which outputs the conditionaed generated data.
Discriminator
The discriminator also receives two inputs: the data (either real or generated) and the conditioning information. It processes both inputs to determine whether the data is real and if it matches the conditioning information.
Data generation
We pass a one-hot-encoded image label as an input of the trained generator network to create new data samples.
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