In this post, you will learn examples of generative adversarial network (GAN). The idea is to put together some of the interesting examples from across the industry to get a perspective on what problems can be solved using GAN. As a data scientist or machine learning engineer, it would be imperative upon us to understand the GAN concepts in a great manner to apply the same to solve real-world problems. This is where GAN network examples will prove to be helpful.
Here are some examples of GAN network usage.
- Text to image translation
- Image editing / manipulating
- Creating images (2-dimensional images)
- Recreating images of higher resolution
- Creating 3-dimensional object
Text to image translation / conversion
GAN can be used to convert / translate text to images. In the example below, the text is translated into images. There are several papers listed on this page in relation to text-to-image translation. Framework such as StackGAN can be used to create photos from text.
Image editing / manipulation
GANs network can be used for image editing. Here is a good read on using GAN for image editing. Here is another paperswithcode page on using GAN for image manipulation. The below picture represents how the place would have looked in winter season.
Recreate Photographs / Images
This is a very interesting use case. One can use GAN for recreating different photographs from same image. Here is a great read on creating photographs using GAN.
Creating images of higher resolution
GAN can be used for creating images of higher resolutions. This can be achieved using Super Resolution GAN (SRGAN). A Super Resolution GAN (SRGAN) is used to upscale images to super high resolutions. An SRGAN uses the adversarial nature of GANs, in combination with deep neural networks, to learn how to generate upscaled images (up to four times the resolution of the original). The photo below represents the image of high resolution using SRGAN.
3D Object Generation
GAN can be used for creating 3-dimensional object. A novel framework, namely 3D Generative Adversarial Network (3D-GAN), generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets.
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