Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a class of deep learning algorithms capable of generating new data instances that resemble your training data. At their core, GANs consist of two neural networks - a generator and a discriminator - pitted against each other in an adversarial process. The generator creates synthetic data samples while the discriminator tries to distinguish these generated samples from real ones. Over time, through this competition, the generator becomes increasingly adept at producing authentic-looking data, enabling GANs to excel in various applications such as image synthesis, semantic manipulation, and style transfer. In the context of Technology/Artificial Intelligence/Artificial Intelligence Algorithms/Deep Learning Algorithms, GANs showcase an innovative approach to deep learning, harnessing the power of competition between networks for enhanced data generation capabilities.