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  2. StyleGAN - Wikipedia

    en.wikipedia.org/wiki/StyleGAN

    This image was generated by an artificial neural network based on an analysis of a large number of photographs. 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.

  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. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    [64] [65] Released in 2022 on Hugging Face's Spaces platform, Craiyon (formerly DALL-E Mini until a name change was requested by OpenAI in June 2022) is an AI model based on the original DALL-E that was trained on unfiltered data from the Internet. It attracted substantial media attention in mid-2022, after its release due to its capacity for ...

  5. Facial composite - Wikipedia

    en.wikipedia.org/wiki/Facial_composite

    The first such system was the drawing-based "Identikit" which was introduced in the U.S. in 1959. [2] A photograph-based system, "Photofit", was introduced in the UK in 1970 by Jacques Penry. [ 2 ] Modern systems are software-based; common systems include SketchCop FACETTE Face Design System Software, Identi-Kit 2000, FACES, E-FIT and PortraitPad.

  6. Human image synthesis - Wikipedia

    en.wikipedia.org/wiki/Human_image_synthesis

    Outputs of the generator network from random input were made publicly available on a number of websites. [ 33 ] [ 34 ] Similarly, since 2018, deepfake technology has allowed GANs to swap faces between actors; combined with the ability to fake voices, GANs can thus generate fake videos that seem convincing.

  7. Eigenface - Wikipedia

    en.wikipedia.org/wiki/Eigenface

    An eigenface (/ ˈ aɪ ɡ ən-/ EYE-gən-) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. [1] The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification.

  8. DeepFace - Wikipedia

    en.wikipedia.org/wiki/DeepFace

    The input is an RGB image of the face, scaled to resolution , and the output is a real vector of dimension 4096, being the feature vector of the face image. In the 2014 paper, [ 13 ] an additional fully connected layer is added at the end to classify the face image into one of 4030 possible persons that the network had seen during training time.

  9. Wojak - Wikipedia

    en.wikipedia.org/wiki/Wojak

    In October 2018, a Wojak with a gray face, pointy nose and blank, emotionless facial expression, dubbed "NPC Wojak", became a popular visual representation for people who cannot think for themselves or make their own decisions, comparing them to non-player characters – computer-automated characters within a video game.

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