GAN Neural Network for Transfer Learning is absolutely creative
Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks
It is an unsupervised learning task in which learns the patter and features from the input and applies the learned model to output a new result which is modeled based on the input weights.
The model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.
GAN is becoming exponentially gaining popularity due to its ability to generate realistic and creative output across a range of problem.
Some Really Great uses of GAN
Image Generation
GAN can be used to produce realistic new images after being trained on dataset of sample images.
Example:
NVIDIA unveiled a new app that turns simple blobs of color drawn by a user into dazzling photorealistic paintings.
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.
Example:
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.
Example:
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.
Example:
Completing missing parts of images:
If you have an image that has some missing parts, GANs can help you to recover these sections.
Example:
Generate Cartoon Characters:
Yanghua Jin, et al. in their 2017 paper titled “Towards the Automatic Anime Characters Creation with Generative Adversarial Networks” demonstrate the training and use of a GAN for generating faces of anime characters
