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Neural style transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation.
The research paper behind the Prisma App technology is called "A Neural Algorithm of Artistic Style" by Leon Gatys, Alexander Ecker and Matthias Bethge and was presented at the premier machine learning conference: Neural Information Processing Systems (NIPS) in 2015. [24] The technology is an example of a Neural Style Transfer algorithm. This ...
The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. [6] [7] In 2021, a third version was released, improving consistency between fine and coarse details in the generator. Dubbed "alias-free", this version was implemented with pytorch ...
[5] [6] The tool allows users to create imitation works of art using the style of various artists. [7] [8] The neural algorithm is used by the Deep Art website to create a representation of an image provided by the user by using the 'style' of another image provided by the user.
Instance normalization (InstanceNorm), or contrast normalization, is a technique first developed for neural style transfer, and is also only used for CNNs. [26] It can be understood as the LayerNorm for CNN applied once per channel, or equivalently, as group normalization where each group consists of a single channel:
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Each generated image starts as a constant array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance).
Gumin's implementation significantly popularised this style of algorithm, with it becoming widely adopted and adapted by technical artists and game developers over the following years. [3] There were a number of inspirations to Gumin's implementation, including Merrell's PhD dissertation, and convolutional neural network style transfer.