Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a class of machine learning models that leverage two neural networks - a generator and a discriminator - in a competitive process to generate new data samples. The generator learns to create synthetic data instances, while the discriminator learns to distinguish these generated instances from real ones obtained from the training dataset. Over time, the generator becomes more proficient at producing realistic data, pushing the boundaries of what is possible with generative models. GANs have been successfully applied in various domains, including image synthesis, semantic image editing, style transfer, and anomaly detection, demonstrating their potential to revolutionize technology and artificial intelligence applications.