Tag: GANs

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Generative Adversarial Networks: The Art of Powerful AI Creativity

Generative Adversarial Networks (GANs) consist of two main components: a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network aims to distinguish between real and fake data. The two networks are trained simultaneously in an adversarial process, pushing each other to improve their performance. Here is a detailed explanation of the architecture and components of GANs.

Generative Adversarial Networks (GANs) - The Basics You Need To Know

Deep Learning – Introduction to Generative Adversarial Networks (GANs)

GANs consist of two neural networks i.e. Generator that generates a fake image of our currency note example and a disa criminator that classifies it into real or fake. The generator’s role is to map the input to the desired data space (image as in the example above). On the other hand second neural network models i.e. the discriminator classify the output with probability as real or fake compared with real datasets.