Neural Style Transfer
A technique that applies the artistic style of one image to the content of another using neural networks.
Overview
Neural style transfer, introduced by Gatys et al. in 2015, uses convolutional neural networks to separate and recombine the content and style of images. Content is captured by higher-layer feature representations, while style is captured by correlations (Gram matrices) between features across layers.
Key Details
The technique optimizes a generated image to simultaneously match the content features of a content image and the style features of a style image. Fast style transfer variants use feedforward networks trained to apply specific styles in real-time. Applications include artistic filters (like Prisma), video stylization, and creative AI tools.