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The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [ 1 ]
This image was generated by an artificial neural network based on an analysis of a large number of photographs. The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, [1] and made source available in February 2019. [2] [3]
Ian J. Goodfellow (born 1987 [1]) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.He is a research scientist at Google DeepMind, [2] was previously employed as a research scientist at Google Brain and director of machine learning at Apple as well as one of the first employees at OpenAI, and has made several ...
Generative adversarial network (GAN) by (Ian Goodfellow et al., 2014) [113] became state of the art in generative modeling during 2014-2018 period. Excellent image quality is achieved by Nvidia 's StyleGAN (2018) [ 114 ] based on the Progressive GAN by Tero Karras et al. [ 115 ] Here the GAN generator is grown from small to large scale in a ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Deep convolutional neural networks using perceptual loss [ edit ] Developed on the basis of the super-resolution generative adversarial network (SRGAN) method, [ 8 ] enhanced SRGAN (ESRGAN) [ 9 ] is an incremental tweaking of the same generative adversarial network basis.
Generative adversarial network; Flow-based generative model; Energy based model; Diffusion model; If the observed data are truly sampled from the generative model, then fitting the parameters of the generative model to maximize the data likelihood is a common method.