What is recorded here?
Proposed learning a generator through a minimax game against a discriminator, with an idealized distribution-matching optimum and an alternating gradient-based implementation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The theoretical analysis assumes a nonparametric setting and an optimal discriminator with suitable updates. The paper explicitly says those proofs do not apply directly to its finite neural-network parameterization. Its experiments are not proof of convergence for arbitrary GAN training, and a generator’s output is not automatically a scientific discovery.
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People and systems
Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron Courville; Yoshua Bengio
Listed source contributors; individual labor shares are not measured
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Documented countries / regions: Canada, France, India
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English catalogue narrative
Generative adversarial networks
Proposed learning a generator through a minimax game against a discriminator, with an idealized distribution-matching optimum and an alternating gradient-based implementation.
Scientific scope
The theoretical analysis assumes a nonparametric setting and an optimal discriminator with suitable updates. The paper explicitly says those proofs do not apply directly to its finite neural-network parameterization. Its experiments are not proof of convergence for arbitrary GAN training, and a generator’s output is not automatically a scientific discovery.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ian J. Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Bing Xu; David Warde-Farley; Sherjil Ozair; Aaron Courville; Yoshua Bengio
Listed source contributors; individual labor shares are not measured