Beyond the Duel: Why the AI World Moved On from GANs (And What Replaced Them)
For years, Generative Adversarial Networks (GANs) were the undisputed rockstars of the artificial intelligence world. Introduced in 2014, the concept of two neural networks—a generator trying to forge fake data and a discriminator trying to catch it—playing a high-stakes game of cat-and-mouse felt revolutionary. GANs gave us hyper-realistic deepfakes, aged portraits, and synthetic datasets that pushed computer vision forward.
Yet, if you look at the generative AI landscape today, GANs have largely stepped out of the spotlight. They haven’t vanished entirely, but they have been eclipsed by architectures that solve the notoriously frustrating quirks of adversarial training.
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