Generative Models
Adversarial network
An adversarial network is a machine learning framework consisting of two neural networks, a generator and a discriminator, that are trained in competition with each other. The generator creates synthetic data samples, while the discriminator evaluates whether the samples are real (from the training dataset) or fake (generated).
Explanation
Adversarial networks, often referred to as Generative Adversarial Networks (GANs), operate through a competitive process. The generator network attempts to produce increasingly realistic data samples to fool the discriminator. Simultaneously, the discriminator network aims to become better at distinguishing between real and generated data. This dynamic continues iteratively, with both networks improving over time. The generator learns to mimic the distribution of the training data, and the discriminator learns to identify subtle differences between the real and generated data. Once trained, the generator can be used to create new data samples that resemble the training data. GANs are used in a variety of applications including image generation, image editing, style transfer, and data augmentation. Some of the challenges of training GANs include mode collapse (where the generator produces only a limited variety of outputs), instability, and difficulty in evaluating the quality of the generated samples.