Beyond the Adversary: Why the AI World is Looking at GAN Alternatives
For years, Generative Adversarial Networks (GANs) were the undisputed rockstars of the synthetic media world. By pitting two neural networks against each other—a generator trying to forge fake data and a discriminator trying to spot the fakes—GANs taught computers how to dream up hyper-realistic human faces, stunning artwork, and rich textures.
Yet, as anyone who has ever tried to train a GAN will tell you, they are notoriously moody. They suffer from training instability, “mode collapse” (where the generator gets lazy and outputs the exact same thing over and over), and a notorious resistance to mathematical convergence.
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