Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two neural networks contest with each other in a game. One network, the generator, produces new data samples, while the other, the discriminator, tries to distinguish whether a sample is real (from the training data) or fake (produced by the generator). By training these networks together, the generator improves at creating realistic data, and the discriminator becomes better at identifying fakes. This approach provides a way to learn deep generative models without requiring Markov chains or unwieldy approximate inference during generation, making it suitable for tasks like image generation and style transfer, which could be relevant to interior design applications.