Minimax loss is an optimization objective that seeks to minimize the worst-case loss over a set of possible conditions, adversaries, or groups. It is used when robustness to the most unfavorable scenario is more important than average performance.
A loss function for generative adversarial networks, based on the cross-entropy between the distribution of generated data and real data. Minimax loss is used in the first paper to describe generative adversarial networks. See Loss Functions in the Generative Adversarial Networks course for more information.