Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are a class of AI systems composed of two neural networks, a 'generator' and a 'discriminator', that compete against each other. The generator creates new data samples, like images or text, while the discriminator tries to distinguish between real data and the fake data produced by the generator. This adversarial process drives both networks to improve, resulting in the generator producing increasingly realistic outputs.
GANs are like an art forger (generator) trying to create a masterpiece that a detective (discriminator) can't tell from the original, constantly improving as the detective gets better at spotting fakes.
They enable the creation of highly realistic synthetic data across various modalities, from images and video to audio and text, with applications in data augmentation and content generation.
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