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Demystifying the Complexities of Generative Adversarial Networks (GANs)
From realistic images to unique music compositions, generative AI is changing the game when it comes to content creation. But what does this mean for the future of human creativity?
Generative Adversarial Networks (GANs) are a type of generative AI that has become increasingly popular in recent years. GANs have been used to create realistic images, music, and even video game levels. However, despite their popularity, GANs can be quite complex and difficult to understand.
Let’s demystify the complexities of GANs and explain how they work.
At its core, a GAN consists of two neural networks: a generator and a discriminator. The generator network takes random noise as input and produces an output that is intended to resemble a specific type of data, such as an image. The discriminator network takes the output of the generator and tries to determine whether it is real or fake. The two networks are trained together, with the goal of the generator producing outputs that are realistic enough to fool the discriminator.
Training a GAN is a complex process that involves a number of steps. First, the generator is trained to produce outputs…
