Generative Adversarial Networks (GANs) For Data Augmentation

Abstract: Generative Adversarial Network is powerful tools for creating new and realistic data to help in to improve machine learning models, specifically when there’s not enough labeled data. It has two parts: Generator-which create a fake data and Discriminator-which tries to tell real data from fake. Through the continuous competition, generator gradually learns to create increasingly realistic data. This paper looks at how GANs can be used to make more data, helping with problems like unbalanced classes and over fitting. It also explains how newer types of GANs, such as Conditional and Wasserstein, increase training stability and enhance the caliber of the data they produce. We also share real-world examples of how GANs are used in different areas, like identifying images analyzing medical scans, and understanding language. These examples show that using GANs to create extra data can really help improve machine learning results. In the final part of paper, we talk about some of the problems that still need to be solved and what the future might look like for this technology. We also explain why it’s important to use both real and fake data carefully, so that models stay accurate and works well.

Generative Adversarial Networks (GANs), Conditional GAN (cGAN), Wasserstein GAN (wGAN)

1. 1.01/Downloads/GENERATIVEADVERSARIALNETWORKSGANSFORDATAAUGMENTATION.pdf [Google Scholar] [Crossref]

2. 2.https://mail.google.com/mail/u/1/#sent/QgrcJHsBpWsMxvBVZFtwBXGwdHTQbZffWFv?projector=1&messagePartId=0.1 [Google Scholar] [Crossref]

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