What are Generative Adversarial Networks (GANs)?
Generative Adversarial Networks (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator. GANs are designed to generate synthetic data that resembles real data by learning from a training dataset. The generator network generates new samples, while the discriminator network evaluates the authenticity of the generated samples compared to the real ones.The generator network takes random input, often referred to as noise or a latent vector, and maps it to the data space. It learns to generate samples that mimic the patterns and distribution of the training data. The goal of the generator is to produce samples that are indistinguishable from real data.Related terms
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