Generative adversarial network in the context of Unsupervised learning

⭐ Core Definition: Generative adversarial 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. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning.

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⭐ Core Definition: Generative adversarial network
Generative adversarial network in the context of Deep learning
Generative adversarial network in the context of Computational creativity
Generative adversarial network in the context of Edmond de Belamy

Generative adversarial network in the context of Deep learning

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised.

Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

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Generative adversarial network in the context of Computational creativity

Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part of computational culture).

Is the application of computer systems to emulate human-like creative processes, facilitating the generation of artistic and design outputs that mimic innovation and originality.

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Generative adversarial network in the context of Edmond de Belamy

Edmond de Belamy, sometimes referred to as Portrait of Edmond de Belamy, is a generative adversarial network (GAN) portrait painting constructed by Paris-based arts collective Obvious in 2018 from WikiArt's artwork database. Printed on canvas, the work belongs to a series of generative images called La Famille de Belamy. The print is known for being sold for US$432,500 during a Christie's auction.

The name Belamy is a pun based on Ian Goodfellow, inventor of GANs. In French, "bel ami" means "good friend", which is an allude to Goodfellow's name.

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Generative adversarial network in the context of Unsupervised learning

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⭐ Core Definition: Generative adversarial network

Generative adversarial network in the context of Deep learning

Generative adversarial network in the context of Computational creativity

Generative adversarial network in the context of Edmond de Belamy