Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization
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Generative Adversarial Networks (GANs) learn to model data distributions through two unsupervised neural networks, each minimizing the objective function maximized by the other. We relate this game theoretic strategy to earlier neural networks playing unsupervised minimax games. (i) GANs can be formulated as a special case of Adversarial Curiosity (1990) based on a minimax duel between two networks, one generating data through its probabilistic actions, the other predicting consequences thereof. (ii) We correct a previously published claim that Predictability Minimization (PM, 1990s) is not based on a minimax game. PM models data distributions through a neural encoder that maximizes the objective function minimized by a neural predictor of the code components.
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