Preferred Label : Generative Adversarial Networks;
MeSH definition : An unsupervised learning network which is composed of two separate networks, a generator
which produces content and a discriminator which works to identify real content from
the generated content. Over time, generative adversarial networks create data that
increasingly resembles the input data.;
Définition CISMeF : A deep learning-based model architecture that normally consists of two competing neural
networks, a generator, and a discriminator. The goal of the “generator” is to synthesize
fake data to fool the “discriminator”, while the “discriminator” tries to discriminate
between the synthesized examples (generator’s output) and the original training data
distribution. The goal of the training is to find a point of equilibrium between the
two competing networks, and after the training process, the generator learns to generate
new data with the same distribution as the training set. This approach can be used
to generate synthetic images.; Source: Adapted from: Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019).
Recent Progress on Generative Adversarial Networks (GANs): A Survey. IEEE Access,
7, 36322–36333. https://doi.org/10.1109/access.2019.2905015External Link Disclaimer
Singh, N. K., & Raza, K. (2021). Medical Image Generation Using Generative Adversarial
Networks: A Review. In Patgiri, R., Biswas, A., Roy, P. (Eds.), Health Informatics:
A Computational Perspective in Healthcare. Studies in Computational Intelligence (pp.
77–96). Springer, Singapore. https://doi.org/10.1007/978-981-15-9735-0_5;
MeSH synonym : Adversarial Network, Generative; Generative Adversarial Network; Network, Generative Adversarial;
CISMeF acronym : GAN;
Origin ID : D000098427;
UMLS CUI : C5940623;
Currated CISMeF NLP mapping
- Record concept(s)
- Semantic type(s)
An unsupervised learning network which is composed of two separate networks, a generator
which produces content and a discriminator which works to identify real content from
the generated content. Over time, generative adversarial networks create data that
increasingly resembles the input data.
A deep learning-based model architecture that normally consists of two competing neural
networks, a generator, and a discriminator. The goal of the “generator” is to synthesize
fake data to fool the “discriminator”, while the “discriminator” tries to discriminate
between the synthesized examples (generator’s output) and the original training data
distribution. The goal of the training is to find a point of equilibrium between the
two competing networks, and after the training process, the generator learns to generate
new data with the same distribution as the training set. This approach can be used
to generate synthetic images.
Source: Adapted from: Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019).
Recent Progress on Generative Adversarial Networks (GANs): A Survey. IEEE Access,
7, 36322–36333. https://doi.org/10.1109/access.2019.2905015External Link Disclaimer
Singh, N. K., & Raza, K. (2021). Medical Image Generation Using Generative Adversarial
Networks: A Review. In Patgiri, R., Biswas, A., Roy, P. (Eds.), Health Informatics:
A Computational Perspective in Healthcare. Studies in Computational Intelligence (pp.
77–96). Springer, Singapore. https://doi.org/10.1007/978-981-15-9735-0_5
