Deep high-resolution style synthesis

A computer system for synthesizing an image style based on a plurality of neural networks, the computer system comprising:

• means for selecting a style image based on user input that identifies the style image;
• means for generating a synthesized image based on a generator neural network and a noise vector of the style image, wherein the generator neural network is a feed-forward generator neural network and is trained using a loss neural network by:
• generating, by different layers of the generator neural network and based on a training image, a first output image at a first resolution and a second output image at a second resolution, wherein the first resolution is smaller than the second resolution,
• applying the loss neural network to the first output image to generate a first set of output style features;
• applying the loss neural network to the second output image to generate a second set of output style features;
• generating a first set of training image style features and a second set of training image style features based on the training image and the loss neural network;
• determining, by the computer system, a first loss based on the first set of output style features and the first set of training image style features, and a second loss based on the second set of output style features and the second set of training image style features;
• and
• adjusting parameters of different layers of the generator neural network based, at least in part, upon the first and second losses;
• and
• means for providing the synthesized image to a user device in response to the user input.

A non-transitory computer-readable storage medium storing instructions that, upon execution by a processor of a computer system, configure the computer system to perform operations comprising:

• selecting a style image based on user input that identifies the style image;
• generating a synthesized image based on a generator neural network and the style image, wherein the generator neural network is a feed-forward generator neural network and is trained using a loss neural network by:
• generating, by different layers of the generator neural network and based on a training image, a first output image at a first resolution and a second output image at a second resolution, wherein the first resolution is smaller than the second resolution, the training image and the style image having a same resolution;
• applying the loss neural network to the first output image to generate a first set of output style features;
• applying the loss neural network to the second output image to generate a second set of output style features;
• generating a first set of training image style features and a second set of training image style features based on the training image and the loss neural network;
• determining, by the computer system, a first loss based on the first set of output style features and the first set of training image style features, and a second loss based on the second set of output style features and the second set of training image style features;
• and
• adjusting parameters of different layers of the generator neural network based, at least in part, upon the first and the second losses;
• and
• providing the synthesized image to a user device in response to the user input.