GAN
GAN stands for Generative Adversarial Network, which is a type of neural network architecture used in unsupervised machine learning. GANs consist of two neural networks, a generator and a discriminator, that are trained together in a game-like setting. The generator creates fake data samples, while the discriminator tries to distinguish between the fake and real data. The two networks are trained iteratively until the generator produces data that is indistinguishable from the real data. GANs have been used for a variety of applications, including image and video generation, data augmentation, and anomaly detection.
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