Search results
Results from the WOW.Com Content Network
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 ]
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 ...
What came out of that fateful meeting was “generative adversarial network” or (GAN), an innovation that AI experts have described as the “coolest idea in deep learning in the last 20 years.”
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".
Meta AI (formerly Facebook) also has a generative transformer-based foundational large language model, known as LLaMA. [48] Foundational GPTs can also employ modalities other than text, for input and/or output. GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49]
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.
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [2] [3] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).
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, and has made several important contributions to the field of deep ...