How to Explore the GAN Latent Space When Generating Faces
Last Updated on September 1, 2020
How to Use Interpolation and Vector Arithmetic to Explore the GAN Latent Space.
Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.
The generative model in the GAN architecture learns to map points in the latent space to generated images. The latent space has no meaning other than the meaning applied to it via the generative model. Yet, the latent space has structure that can be explored, such as by interpolating between points and performing vector arithmetic between points in latent space which have meaningful and targeted effects on the generated images.
In this tutorial, you will discover how to develop a generative adversarial network for face generation and explore the structure of latent space and the effect on generated faces.
After completing this tutorial, you will know:
- How to develop a generative adversarial network for generating faces.
- How to interpolate between points in latent space and generate images that morph from one face to another.
- How to perform vector arithmetic in latent space and achieve targeted results in the resulting generated faces.
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