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  3. Artbreeder - Wikipedia

    en.wikipedia.org/wiki/Artbreeder

    Artbreeder, formerly known as Ganbreeder, [4] is a collaborative, machine learning-based art website. Using the models StyleGAN and BigGAN , [ 4 ] [ 5 ] the website allows users to generate and modify images of faces, landscapes, and paintings, among other categories.

  4. Fotor - Wikipedia

    en.wikipedia.org/wiki/Fotor

    Fotor's software is available on mobile devices, desktop computers, and online as a web app. [5] It offers a suite of image editing tools and features for users to edit and enhance photos quickly and easily. Fotor One-Tap Photo Enhancer is a tool that is designed to automatically improve image quality and fix common imaging problems.

  5. Art name - Wikipedia

    en.wikipedia.org/wiki/Art_name

    An art name (pseudonym or pen name), also known by its native names hào (in Mandarin Chinese), gō (in Japanese), ho (in Korean), and tên hiệu (in Vietnamese), is a professional name used by artists, poets and writers in the Sinosphere.

  6. Generative art - Wikipedia

    en.wikipedia.org/wiki/Generative_art

    Generative art is post-conceptual art that has been created (in whole or in part) with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist.

  7. Union List of Artist Names - Wikipedia

    en.wikipedia.org/wiki/Union_List_of_Artist_Names

    Sample entry, for Mark Rothko, screen 1 Sample ULAN Record, Mark Rothko continued.. The Union List of Artist Names (ULAN) is a free online database of the Getty Research Institute using a controlled vocabulary, which by 2018 contained over 300,000 artists and over 720,000 names for them, as well as other information about artists. [1]

  8. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. [43] Unlike previous algorithmic art that followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. [12]

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