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Retrieved August 15, 2023. Generative artificial intelligence (AI) will become one of the most important features for creative designers and marketers. Adobe on Tuesday unveiled a Generative Fill feature in Photoshop to bring Firefly's AI capabilities into design. ^ Michael Nuñez (July 19, 2023).
A generative pre-trained transformer (GPT) is a type of large language model (LLM) [1][2][3] and a prominent framework for generative artificial intelligence. [4][5] It is an artificial neural network that is used in natural language processing by machines. [6] It is based on the transformer deep learning architecture, pre-trained on large data ...
P ( X , Y ) {\displaystyle P (X,Y)} on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances (outcomes) of an observation x. [2] A discriminative model is a model of the conditional probability. P ( Y ∣ X = x ) {\displaystyle P (Y\mid X=x)} of the target Y, given an observation x.
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. [1][2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [3] In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain ...
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. [ 15 ]
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1][2][3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one. The direct modeling of likelihood provides ...
BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learned by self-supervised learning to represent text as a sequence of vectors. It had the transformer encoder architecture. It was notable for its dramatic improvement over ...
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