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Generative Adversarial Network
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
- GitHub: https://github.com/topics/generative-adversarial-network
- Wikipedia: https://en.wikipedia.org/wiki/Generative_adversarial_network
- Released: June 10, 2014
- Related Topics: deep-learning, neural-network,
- Aliases: gan, generative-adversarial-networks,
- Last updated: 2025-02-13 00:12:09 UTC
- JSON Representation
https://github.com/georgiosioannoucoder/realesrgan
Implementation of the paper "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data." 🖼️
computer-vision data-science discriminator esrgan gan generative-adversarial-network generator image-processing machine-learning opencv-python real-esrgan super-resolution
Last synced: 02 Jan 2025
https://github.com/narumiruna/mnist-gans
gan generative-adversarial-network lsgan mnist pytorch wasserstein-gan wgan wgan-gp
Last synced: 31 Jan 2025
