How can latent space navigation in GANs be applied to generate variations of realistic images

Question

With the help of programming and examples, can you tell me how latent space navigation in GANs can be applied to generate variations of realistic images?

Answer 1

You can steer the generator to produce diverse outputs. This can be done by interpolating between latent vectors or adding controlled noise variations.

Here's an example of latent space manipulation:

In the above code we are using the following key points:

• Latent Interpolation: Interpolating between two latent vectors generates smooth transitions between images.
• Control Variations: By adjusting latent vectors, you can control the degree of variation in generated images.
• Realistic Variations: Small changes in the latent space can produce realistic variations of images (e.g., different poses, styles, or features).

Hence, by referring to above, you can apply navigation in GANs to generate variations of realistic images.