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Generative Adversarial Networks (GANs) are a type of neural network architecture comprising two networks – a generator and a discriminator – trained simultaneously through competition. The generator creates synthetic data, while the discriminator tries to distinguish between real and fake data. GANs are used in AI for tasks like image generation, data augmentation, and style transfer, enabling the creation of realistic synthetic data with various applications across industries.
GANs are a type of AI model consisting of two neural networks, a generator and a discriminator, trained simultaneously in a competitive manner. The generator generates synthetic data samples, while the discriminator tries to distinguish between real and fake samples. GANs are commonly used for generating realistic images, videos, and other types of media.