In which scenarios are generative adversarial networks commonly used?

Generative Adversarial Networks (GANs) are primarily known for their capability to generate new data that resembles training data. These neural networks consist of a generator that creates data and a discriminator that evaluates it, promoting an adversarial relationship that fosters high-quality outputs from the generator.

Image generation is one of the most prominent applications of GANs. They excel in creating realistic images from random noise, generating new samples that mimic the distribution of the training set. This includes creating artwork, photorealistic images, and even enhancing existing images.

Data augmentation is another significant use case for GANs. They can produce various samples from a limited dataset, enriching the training data available for other machine learning models and helping improve their robustness and generalization.

While information retrieval and semantic segmentation are essential tasks in AI, they do not typically employ GANs as their foundational technology. Information retrieval focuses on finding relevant data points or documents based on user queries, while semantic segmentation involves classifying each pixel in an image into distinct categories, often utilizing convolutional neural networks (CNNs) instead.

Therefore, the scenarios where GANs have established their effectiveness are primarily in image generation and data augmentation, making these applications highly relevant and widely researched in the domain of artificial intelligence.