Unsupervised learning is at the core of Generative AI. It’s a machine learning paradigm where models learn from unlabeled data, finding hidden patterns and structures. Generative AI leverages unsupervised learning to create data or content that resembles human-generated data.
For example, unsupervised learning is used in Generative Adversarial Networks (GANs), a popular Generative AI architecture. GANs consist of a generator and a discriminator network that compete with each other. The generator aims to create realistic data (like images or text), while the discriminator tries to distinguish between real and generated data. This adversarial process drives the generator to produce increasingly authentic content.
