Generative Adversarial Networks for Image Augmentation: Architecture, Training, and Real World Applications

Generative Adversarial Networks (GANs) have become a powerful tool in deep learning, especially for creating realistic images. At the heart of a GAN are two neural networks — a generator and a discriminator — that learn by challenging each other in a game-like setting. This paper describes how GANs work, focusing on their structure, how they are trained, and how they can be used for image augmentation. Taking a closer look at how the design of the generator and discriminator affects the quality of the images produced, a comparison of GAN-based image augmentation with traditional techniques is done to understand where GANs offer real advantages, and where they may fall short. Finally, a discussion on how GANs are being used in real-world applications and what their growing role means for the future of image-based machine learning is done.