A Generative Adversarial Network (GAN) is a type of machine learning framework used to generate new, synthetic data that resembles a given dataset. It was introduced by Ian Goodfellow in 2014 and has since become a foundational technology in the field of generative AI.
A GAN consists of two main components: a Generator and a Discriminator. These two neural networks are trained simultaneously in a process that is often described as a "game." The generator’s job is to create realistic data (such as images, audio, or text), while the discriminator’s job is to evaluate whether the data it receives is real (from the dataset) or fake (produced by the generator).
During training, the generator tries to fool the discriminator by producing more and more realistic data. Meanwhile, the discriminator becomes better at telling fake from real. This adversarial process continues until the generator becomes good enough that the discriminator can no longer reliably tell the difference. At this point, the generator is considered trained, and it can be used to create new, convincing samples.
One of the key challenges in GANs is achieving a stable training process, as the balance between the generator and discriminator must be carefully maintained. If one network becomes too strong, the training can collapse. Techniques such as feature matching, label smoothing, and Wasserstein loss have been developed to improve stability.
GANs are widely used in applications such as image generation, video synthesis, text-to-image models, deepfake creation, and even drug discovery. Their ability to generate realistic, high-quality outputs has made them a central technology in the development of synthetic media and creative tools.
To gain hands-on experience in building and applying models like GANs, you can explore a Generative AI Course with Placement.
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