Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6755245870683365376
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GAN is comprised of two nets, the “generator” generates new instances of data and the “discriminator” evaluates them for authenticity. The discriminator, which is a standard CNN, tries to determine whether a specific instance of data belongs to the actual training dataset or not. The generator is like an inverse CNN, which given random numbers generates an image. The goal of the generator is to pass fake images as authentic to the discriminator which then evaluates the images for authenticity based on its ground truth of real images.
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