Arjun Jain | Fast Code AI

No, PM Is Not a GAN. Stop! Jürgen Schmidhuber's Predictability Minimization (1992) and Ian Goodfellow's GANs (2014) both use adversarial objectives. So does every zero-sum game since von Neumann. That's where the similarity ends. Goodfellow's generator never sees real data. It maps noise to samples and learns the data distribution purely through the discriminator's gradients. That's the whole trick. That's the invention. Schmidhuber's PM does the opposite - both players sit on top of the same real data, competing to learn independent features. It's representation learning. Nothing is generated. No noise is mapped anywhere. No distribution is learned. Calling PM a GAN because both use minimax is like calling chess a war because both have strategy. PM was a smart idea about feature independence. GANs were a breakthrough in implicit generative modeling. These are not the same insight, and retroactively collapsing the distance between them doesn't honor prior work - it misrepresents both.