10 Project Ideas Based on Generative Adversarial Networks (GAN)

Top 10 project ideas that are based on generative adversarial networks for your next project

Earlier this year, MyHeritage's Deep Nostalgia, which used AI technology to bring old photos to life, took the world by storm. People all over the world were captivated by the results because seeing your deceased loved ones in action again, smiling and moving, was an emotional experience. Images of many famous people, including freedom fighters, renowned scientist's activists, researchers, and authors, were animated and shared on social media platforms.

This article explains the 10 best project ideas based on the generative adversarial networks that will help you grow into your next project. Read to know more about project ideas based on GAN.

1. Create your own Anime Characters using GANs

Have you heard of Fullmetal Alchemist? Bebop Cowboy? What is a Death Note? You have, of course! Who hasn't heard of these well-known anime series? Even if you don't, it's never too late to try something different. We can create our own Anime characters' faces using GANs. Several GAN architectures can be used for this task, including DCGAN, StyleGAN, and others.

2. Image Style Transfer using CycleGANs

Have you ever wished to paint like Monet or Van Gogh? What if we tell you that you can now without even picking up a paintbrush? GAN is all you need. Image Style Transfer is a task that takes two images as input, namely content, and reference, and produces a composition that combines the objects of the content image in the style of the reference image. By style, we mean the reference image's brush strokes, colors, and textures.

3. Semi-Supervised GAN (SGAN) using the MNIST dataset

Semi-Supervised Learning is a novel type of problem in which the training dataset contains both labeled and unlabeled data. The difficult aspect of this problem is that the model is expected to learn from a small set of labeled data and then apply that knowledge to classify unlabeled data as well as generalize on previously unseen data. GANs are essentially a combination of supervised and unsupervised algorithms.

4. Using GANs for Pulmonary Chest X-rays and Medical Image Synthesis

As AI advances at the speed of light, dedicated research is being conducted to find novel ways to exploit sophisticated deep-learning models in the field of medical science. It will not only relieve the burden on frontline medical service providers, but it will also aid in more effectively dealing with situations such as pandemics/epidemics. Many CNNs have demonstrated cutting-edge performance in predicting correct diagnosis using radiography and CT scans.

5. Build Face Aging Application using Face Synthesis

Who doesn't want to know how we'd look in our 60s and 70s when we'd be frail and wrinkled? While we don't have Professor Trelawney's magical crystal ball to see into the future, we can certainly use generative modeling to predict how we will look in the future. We can render a face with natural aging that is aesthetically blended using cGANs or Conditional GANs. It is especially useful in the search for kidnapped children.

6. Colourizing Black and White Images using GANs

Working on image colorization is a fun GAN project. We all have old photographs and reels from the days when colored filmography was the talk of the town. Wouldn't it be fantastic if you could colorize those black-and-white images and bring them to life?

7. Removing Unwanted Noises from Real Scene Images using GANs

Have you ever taken a picture of the perfect sunset with just the right amount of reds and yellows on your phone? However, the texture of the image was too grainy, detracting from the quality of the capture. It had the potential to be the next Instagram viral picture, but you can't show it to anyone now. So, there's nothing to be concerned about. GANs will help you.

8. Create a Text-to-Image synthesizer using ST-GANs

Creating photorealistic images from textual descriptions is both an exciting and difficult problem. GANs have struggled to generate photorealistic images based on other images fed into them. The only input given in this task is a textual description of the image, where many details of the expected/target can be ambiguous, and the model is expected to make its assumptions. When high-resolution images must be generated, details and finesse become a major issue.

9. Abstractive Text Summarizer using GANs

There are two types of text summarization tasks: 1) Text Extraction and Summarization and 2) Summarization of Abstractive Text. Extractive Text Summarization is a relatively simple task in which we extract the sentences that best represent the text from a long text. Abstractive Text Summarization, on the other hand, is difficult because we must paraphrase the long text to capture the main ideas in a short text. It may or may not contain sentences or even words from the original text.

10. Anomaly Detection using GANs in MNIST Datasets

Anomaly detection is the process of identifying unusual data points from a set of data. While most anomaly detection models work well with low-dimensional data, anomaly detection in high-dimensional, complex data is also required. This data includes audio, image, and video files. They frequently require manual intervention in terms of feature engineering and extraction, which can be a barrier to extracting the best performance from the ML model.