The buzz around Deep-fakes has reached far and wide, further it has been a candidate of conversation for several months. Let’s understand what the buzz is all about.
Let’s look into the greatest hits and the most impressive applications by GANs (Generative Adversarial Networks), before we take a deep dive into the depths of the algorithm.
- Single Image Super-Resolution (SRGAN): An example of industrial application is of producing high resolution magnetic resonance (MR) images faster and cutting wait times.
- Perceptual GAN (PGAN): Can we find a way to identify objects in a low resolution, noisy images? PGAN’s might be able to help you out.
- Text to Photo-realistic Image Synthesis (StackGAN): Have you ever thought of creating an image out of thin air. This implementation of GAN’s takes a stab at such a challenging problem.
- High Resolution Image Synthesis: Self Driving cars need a lot of training data to learn how to drive safely. You have to start somewhere, and this is one of the techniques that help generate videos for training.
This type of image synthesis is a form of conditional GAN’s which have been known for several applications.
The list above is just a preview of some of the applications of generative adversarial networks (GAN). We at data science discovery, also felt inspired and started on our journey to discover this concept and dive into the depths of this topic.
I looked at several whitepapers to get familiar with this topic. Further, one of my fellows, Navin Manaswi, who at the time was working on a new book “Generative Adversarial Networks with Industrial Use Cases” helped out by sharing some of the chapters he had written.
Me and my colleague also decided to try and experiment with this technique on one of our ongoing projects. In most examples that we have seen, it is evident that it works well on images but what about structured data. However, that is a whole another story.
In the next article we dive deeper into the concept by building an intuition and learning about the architecture of these neural networks.
- Generative Adversarial Nets
- Goodfellow et al 2016
- Salimans et al 2016
- Training Process Improvement for GANs
- Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
- The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
- Progressive Growing of GANs
- A Style-Based Generator Architecture for Generative Adversarial Networks
- Generative Adversarial Networks with Industrial Use Cases, Navin Manaswi (Book)
- Single Image Super-Resolution (SRGAN)
- Perceptual GAN (PGAN)
- Text to Photo-realistic Image Synthesis (StackGAN)
- High Resolution Image Synthesis
- Image-to-Image Translation
- Deep Privacy
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About the writers:
- Ankit Gadi: Driven by a knack and passion for data science coupled with a strong foundation in Operations Research and Statistics has helped me embark on my data science journey.