generative adversarial network (gan) layers
Generative adversarial network (GAN) layers are components used in the architecture of a GAN, a type of machine learning model. These layers consist of two interconnected networks: a generator network that tries to produce realistic data samples from random noise, and a discriminator network that tries to differentiate between the generated data and real data. These layers work together in a competitive manner, with the generator trying to fool the discriminator, and the discriminator striving to correctly classify the data. Through this adversarial process, GAN layers enable the model to learn and generate high-quality synthetic data.
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