Examples of Generative Adversarial Network (GAN)

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.

GAN examples - Text to image translation using GAN
Fig 1. Text to image translation

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.

GAN examples - Image editing with GAN
Fig 2. Image editing with GAN

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.

GAN example - Creating images using GAN
Fig 3. 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.

Fig 4. SRGAN used for creating photo of high resolution

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.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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