GANs have some fairly useful practical applications, which include the following:
- Image generation: Generative networks can be used to generate realistic images after being trained on sample images. For example, if we want to generate new images of dogs, we can train a GAN on thousands of samples of images of dogs. Once the training has finished, the generator network will be able to generate new images that are different from the images in the training set. Image generation is used in marketing, logo generation, entertainment, social media, and so on. In the next chapter, we will be generating faces of anime characters.
- Text-to-image synthesis: Generating images from text descriptions is an interesting use case of GANs. This can be helpful in the film industry, as a GAN is capable of generating new data based on some text that you have made up. In the comic industry, it is possible to automatically generate sequences of a story.
- Face aging: This can be very useful for both the entertainment and surveillance industries. It is particularly useful for face verification because it means that a company doesn't need to change their security systems as people get older. An age-cGAN network can generate images at different ages, which can then be used to train a robust model for face verification.
- Image-to-image translation: Image-to-image translation can be used to convert images taken in the day to images taken at night, to convert sketches to paintings, to style images to look like Picasso or Van Gogh paintings, to convert aerial images to satellite images automatically, and to convert images of horses to images of zebras. These use cases are ground-breaking because they can save us time.
- Video synthesis: GANs can also be used to generate videos. They can generate content in less time than if we were to create content manually. They can enhance the productivity of movie creators and also empower hobbyists who want to make creative videos in their free time.
- High-resolution image generation: If you have pictures taken from a low-resolution camera, GANs can help you generate high-resolution images without losing any essential details. This can be useful on websites.
- Completing missing parts of images: If you have an image that has some missing parts, GANs can help you to recover these sections.
