GAN-powered data augmentation is a term from Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. It describes a modern method in which so-called Generative Adversarial Networks (GANs) – a special form of artificial neural networks – are used to generate additional training data for computer programs.
Imagine a company wants to develop an AI that can analyse X-ray images. However, there aren't enough real X-ray images for training. This is where GAN-powered data augmentation comes into play: the AI automatically generates many artificial, but realistic-looking X-ray images that help the system learn better.
This way, researchers or companies can significantly increase the performance of their artificial intelligence without having to collect expensive or hard-to-obtain original data. So the main benefits are better results, time and cost savings. Particularly in areas where real data is scarce or sensitive, GAN-powered data augmentation brings about major progress – for example, in medicine, autonomous driving or in the detection of fraudulent activities.
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