How to Develop a Conditional GAN (cGAN) From Scratch
Last Updated on September 1, 2020
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.
Although GAN models are capable of generating new random plausible examples for a given dataset, there is no way to control the types of images that are generated other than trying to figure out the complex relationship between the latent space input to the generator and the generated images.
The conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by a generator model. Image generation can be conditional on a class label, if available, allowing the targeted generated of images of a given type.
In this tutorial, you will discover how to develop a conditional generative adversarial network for the targeted generation of items of clothing.
After completing this tutorial, you will know:
- The limitations of generating random samples with a GAN that can be overcome with a conditional generative adversarial network.
- How to develop and evaluate an unconditional generative adversarial network for generating photos of items of clothing.
- How to develop and evaluate a conditional generative adversarial network for
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