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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]
The original GAN method is based on the GAN game, a zero-sum game with 2 players: generator and discriminator. The game is defined over a probability space (,,), The generator's strategy set is the set of all probability measures on (,), and the discriminator's strategy set is the set of measurable functions : [,].
The company integrated Gemini Pro into its Bard chatbot and announced plans for "Bard Advanced" powered by the larger Gemini Ultra model. [53] In February 2024, Google unified Bard and Duet AI under the Gemini brand, launching a mobile app on Android and integrating the service into the Google app on iOS .
Vanilla software refers to applications and systems used in their unmodified, original state, as distributed by their vendors. [1] This term is often applied in fields such as enterprise resource planning (ERP), [2] e-government systems, [3] and software development, where simplicity and adherence to vendor standards are more important than expanded functionality. [4]
In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions, organized in layers, notable for being able to distinguish data that is not linearly separable.
A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
The 5.1 model is more opinionated than version 5, applying more of its own stylization to images, while the 5.1 RAW model adds improvements while working better with more literal prompts. The version 5.2 included a new "aesthetics system", and the ability to "zoom out" by generating surroundings to an existing image. [ 16 ]