Skill

Generative Adversarial Networks (GAN)

Data Science, Analytics and AI/ML

A Generative Adversarial Network (GAN) is a machine learning architecture, introduced by Ian Goodfellow in 2014, consisting of two neural networks -- a generator and a discriminator -- trained together in competition, where the generator creates synthetic data and the discriminator tries to distinguish it from real data. This adversarial training process enables GANs to produce highly realistic images, video, and audio. Machine learning engineers and researchers use GANs for applications like image synthesis, deepfakes, data augmentation, and style transfer.

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