What does "image synthesis" refer to in the context of GAN applications?

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In the context of GAN (Generative Adversarial Networks) applications, "image synthesis" specifically refers to the generation of new images from random noise or low-dimensional representations. GANs operate by using two neural networks—the generator and the discriminator—in a competitive setting. The generator creates new image data, while the discriminator evaluates the authenticity of the generated images against real images. This process allows the generator to learn to produce images that are increasingly realistic over time.

By starting with random noise, the generator transforms this input into coherent images that mimic the underlying distribution of the training dataset. This capability is particularly powerful because it enables the creation of completely novel images that did not exist before, as opposed to simply modifying or enhancing existing images. This characteristic of GANs makes them a crucial tool for tasks like creating artworks, generating realistic human faces, or synthesizing any other type of image where new content is required.

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