Generative Adversarial Nets.

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., et al. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems.

Type

Book chapter

Intellectual base

Review or monograph

Year

2014

Link

https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html

Cited from

economics_finance, mathematics

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Minimax Strategy
  • In statistics and machine learning, minimax estimators minimize worst-case risk over the parameter space, robust estimation is minimax over contamination neighborhoods, and adversarial training, certified robustness, and GAN training are explicitly minimax over perturbations or over a generator-discriminator game.

    This sourceFormulates GAN training as an explicit minimax (two-player) game between generator and discriminator; the adversarial-robustness lineage extends to adversarial training.

• Zero Sum Game
  • The pattern ports further. The minimax structure into machine learning: the zero-sum game-theory of optimal play transferred into adversarial training and robust optimisation, where two agents' opposed objectives instantiate the fixed-total structure formally.

    This sourceCasts adversarial training as a two-player minimax game with opposed objectives — the machine-learning instantiation of the fixed-total/minimax structure.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://papers.nips.cc/paper/5423-generative-adversarial-nets ×1

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