Adversarial Networks, in particular Generative Adversarial Networks (GANs), are a class of AI systems that consist of two competing neural networks. GANs were introduced by Ian Goodfellow in 2014 and are used to generate synthetic data that closely resembles real-world data. This approach has revolutionized several areas, including image generation, data augmentation and unsupervised learning.
A GAN consists of two main components: the generator and the discriminator. The generator produces synthetic data, such as images, while the discriminator compares this data with real data. The generator aims to produce data that is indistinguishable from real data, while the discriminator endeavors to distinguish between real and generated data. This adversarial process continues until the generator produces very realistic data that the discriminator can no longer reliably distinguish from real data.
During the training process of a GAN, the generator creates synthetic patterns, which are then evaluated by the discriminator. The discriminator provides feedback on the realism of the samples and the generator adjusts its parameters to improve the quality of its results. This iterative process is like a game of cat and mouse, where the generator and discriminator continuously improve in response to each other’s actions.
GANs have numerous applications. In image processing, they can generate high-resolution images from low-resolution input, create realistic human faces and even convert images from one style to another, e.g. turning sketches into photorealistic images. In the field of video games, GANs can create realistic textures and environments. In addition, GANs are used in the medical field to create synthetic medical images for research and training purposes when real data is scarce.
Despite their capabilities, adversarial networks face some challenges. Training GANs can be difficult due to the delicate balance between the generator and the discriminator. If one network becomes too strong compared to the other, the training process can become unstable. In addition, GANs are known to suffer from mode collapse, where the generator only produces a limited number of samples and cannot capture the diversity of the real data distribution.
