👉 Generators are a class of neural network models primarily used in Generative Adversarial Networks (GANs) for tasks like image generation, text synthesis, and data augmentation. At their core, generators consist of a series of layers that transform a random noise vector, often drawn from a standard normal distribution, into a data representation that mimics the input data distribution. These layers typically include convolutional or fully connected layers, followed by non-linear activation functions, and often normalization steps to stabilize training. During training, the generator aims to produce outputs that are indistinguishable from real data, while a discriminator network evaluates the authenticity of these outputs. The interplay between the generator and discriminator drives the generator to improve its output quality, ultimately learning to map random noise to realistic data. This adversarial process is key to the generator's ability to create high-fidelity synthetic samples.
