@article{neuralstyletransfer2015,
  title = {A Neural Algorithm of Artistic Style (neural style transfer)},
  author = {Gatys, L. A. and Ecker, A. S. and Bethge, M.},
  year = {2016},
  journal = {CVPR 2016 (IEEE/CVF)},
  url = {https://arxiv.org/abs/1508.06576},
  abstract = {Gatys, Ecker and Bethge (2015/2016) showed that the Gram matrices of convolutional feature maps from a pretrained VGG network encode visual style, and that content and style can be independently recombined by optimising an image against both a content loss and a style loss. The result is an input photograph rendered in the visual idiom of any reference artwork without paired training examples. The paper founded the neural-style-transfer subfield, triggered a wave of commercial style-transfer apps, and remains the conceptual root of style conditioning in diffusion-era tools such as ControlNet and LoRA. It appears in nearly every creative-AI curriculum as the starting point for discussions of neural stylisation and AI-assisted artwork.},
  keywords = {generative-ai, image-generation, studio-pedagogy},
  note = {AI \& Animation Education Knowledge Base}
}