Patent ID: 11881003
Assignee: DEEP RENDER LTD.
Field: Audio-visual technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 0:
1. A computer-implemented method of training an image generative network fθ for a set of training images, in which an output image {circumflex over (x)} is generated from an input image x of the set of training images non-losslessly, and in which a proxy network is trained for a gradient intractable perceptual metric that evaluates a quality of an output image {circumflex over (x)} given an input image x, the method of training using a plurality of scales for input images from the set of training images, the method including the steps of:
(i) receiving an input image x of the set of training images and generating one or more images which are derived from x to make a multiscale set of images {xi} which includes x;
(ii) the image generative network fθ generating an output image {circumflex over (x)}i from an input image xiϵ{xi}, without tracking gradients for fθ;
(iii) the proxy network outputting an approximated function output ŷi, using the xi and the {circumflex over (x)}i as inputs;
(iv) the gradient intractable perceptual metric outputting a function output yi, using the xi and the {circumflex over (x)}i as inputs;
(v) evaluating a loss for the proxy network, using the yi and the ŷi as inputs, and including the evaluated loss for the proxy network in a loss array for the proxy network;
(vi) repeating steps (ii) to (v) for all the images xi in the multiscale set of images {xi};
(vii) using backpropagation to compute gradients of parameters of the proxy network with respect to an aggregation of the loss array assembled in executions of step (v);
(viii) optimizing the parameters of the proxy network based on the results of step (vii), to provide an optimized proxy network;
(ix) the image generative network fθ generating an output image {circumflex over (x)}i from an input image xiϵ{xi};
(x) the optimized proxy network outputting an optimized approximated function output ŷi, using the {circumflex over (x)}i and the {circumflex over (x)}i as inputs;
(xi) evaluating a loss for the generative network fθ, using the xi, the {circumflex over (x)}i and the optimized approximated function output ŷi as inputs, and including the evaluated loss for the generative network fθ in a loss array for the generative network fθ;
(xii) repeating steps (ix) to (xi) for all the images xi in the multiscale set of images {xi};
(xiii) using backpropagation to compute gradients of parameters of the generative network fθ with respect to an aggregation of the loss array assembled in executions of step (xi);
(xiv) optimizing the parameters of the generative network fθ based on the results of step (xiii), to provide an optimized generative network fθ, and
(xv) repeating steps (i) to (xiv) for each member of the set of training images.