Generative Adversarial Networks (GANs) generate images or videos through a process involving two networks: a generator and a discriminator. The generator creates synthetic data (e.g., images), while the discriminator evaluates the authenticity of the generated data by distinguishing it from real samples. This adversarial setup allows the generator to improve over time, producing increasingly realistic outputs.
The generator starts with random noise (e.g., Gaussian noise) as input and uses a series of transformations to create structured outputs resembling the target domain. For example, in image generation, the generator learns to map noise into detailed images by optimizing against the discriminator's feedback. Each iteration improves the generator's ability to mimic real data, guided by the discriminator's classification errors.
GANs can also generate videos by extending the generator's architecture to handle temporal information. Techniques like 3D convolutions or recurrent layers enable the generator to model time-dependent patterns. For example, a GAN trained on video data might learn to generate smooth transitions and realistic motion sequences. Despite their power, GANs require careful training to avoid issues like mode collapse, where the generator produces limited variations of outputs.