Machine learning
Generative Adversarial Network
A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗
- Karras, T. et al. (2020). Analyzing and Improving the Image Quality of StyleGAN. CVPR. DOI: 10.1109/CVPR42600.2020.00813 ↗
Related methods
Referenced by
Adversarial TrainingCycleGANDomain-adaptive GANDomain-adaptive variational autoencoderExplainable GANFine-Tuned Generative Adversarial NetworkLoRA and PEFTMultilingual GANMultimodal GANMultimodal Variational AutoencoderNeural Style TransferSelf-supervised Diffusion ModelSelf-supervised GANSelf-supervised Image ClassificationSelf-supervised Variational AutoencoderSemi-supervised Diffusion ModelSemi-supervised GANSemi-supervised Variational AutoencoderSynthetic Data GenerationTransfer learning GANTransfer learning variational autoencoderVariational AutoencoderVision TransformerWasserstein GANWeakly Supervised Diffusion ModelWeakly supervised GANWeakly Supervised Variational Autoencoder