Which application is NOT typically associated with the use of GANs?

The application of data compression is not typically associated with the use of Generative Adversarial Networks (GANs). GANs are primarily used for generating new data samples that mimic a training dataset, and they excel in generating high-quality images, audio, and sometimes text, through adversarial training involving a generator and a discriminator.

Image generation is perhaps the most well-known application of GANs, where they can produce realistic images from random noise or modify existing images. Text generation using GANs is less common than with models specifically designed for text, but there are instances where GANs have been creatively adapted for text synthesis. Similarly, GANs can be employed in audio synthesis to create realistic audio clips by learning from a training set of audio data.

In contrast, data compression involves reducing the size of data for storage or transmission without significant loss of quality. While various techniques exist for data compression, GANs are not the primary method used for this purpose. Other methods, such as Huffman coding or transform coding, are more effective and suitable for compressing data. Therefore, data compression stands out as an application that is not typically associated with GANs.