Generative Adversarial Networks (GANs) generate images or videos by training two neural networks—a generator and a discriminator—in a competitive process. The generator creates synthetic data (like images) from random noise, while the discriminator evaluates whether the data is real (from a training dataset) or fake (produced by the generator). The generator aims to fool the discriminator, and the discriminator improves at detecting fakes over time. This adversarial interaction pushes the generator to produce increasingly realistic outputs. For example, when generating faces, the generator might start with random pixel patterns and refine them into coherent facial features through repeated training cycles.
The training process involves alternating updates to both networks. First, the generator takes a random vector (noise) as input and outputs an image. This image is fed to the discriminator alongside real images from the dataset. The discriminator’s predictions (real vs. fake) are used to calculate loss for both networks. The generator’s loss measures how well it tricked the discriminator, while the discriminator’s loss reflects its accuracy. Backpropagation adjusts the generator’s parameters to minimize its loss and the discriminator’s parameters to minimize its own. Over time, the generator learns to map noise to data distributions that resemble the training set. For videos, this process extends to sequential data: the generator might produce frames in a temporal sequence, and the discriminator evaluates both individual frames and their coherence over time.
Practical implementations often face challenges. For instance, mode collapse occurs when the generator produces limited varieties of outputs (e.g., the same face repeatedly). Techniques like minibatch discrimination (where the discriminator evaluates batches of samples instead of individual ones) or Wasserstein GANs (which use a different loss function) help mitigate this. Applications range from creating photorealistic images (e.g., NVIDIA’s StyleGAN for human faces) to video synthesis (e.g., generating animated characters). Developers typically use frameworks like TensorFlow or PyTorch, leveraging convolutional layers in the generator for upsampling noise and in the discriminator for downsampling images. While GANs require careful tuning, their ability to learn complex data distributions makes them a powerful tool for synthetic media generation.