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

Claim 18:
19. A computer system configured to train an image generative network fθ for a set of training images, in which the system generates an output image {circumflex over (x)} 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, wherein the computer system is configured to:
(i) receive an input image x from the set of training images and generate one or more images which are derived from x to make a multiscale set of images {xi} which includes x;
(ii) use the image generative network fθ to generate an output image {circumflex over (x)}i from an input image xiϵ{xi}, without tracking gradients for fθ;
(iii) use the proxy network to output an approximated function output ŷi, using the xi and the {circumflex over (x)}i as inputs;
(iv) use the gradient intractable perceptual metric to output a function output yi, using the xi and the {circumflex over (x)}i as inputs;
(v) evaluate a loss for the proxy network, using the yi and the ŷi as inputs, and to include the evaluated loss for the proxy network in a loss array for the proxy network;
(vi) repeat (ii) to (v) for all the images xi in the multiscale set of images {xi};
(vii) use backpropagation to compute gradients of parameters of the proxy network with respect to an aggregation of the loss array assembled in executions of (v);
(viii) optimize the parameters of the proxy network based on the results of (vii), to provide an optimized proxy network;
(ix) use the image generative network fθ to generate an output image {circumflex over (x)}i from an input image xiϵ{xi};
(x) use the optimized proxy network to output an optimized approximated function output ŷi, using the xi and the {circumflex over (x)}i as inputs;
(xi) evaluate 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 to include the evaluated loss for the generative network fθ in a loss array for the generative network fθ;
(xii) repeat (ix) to (xi) for all the images xi in the multiscale set of images {xi};
(xiii) use backpropagation to compute gradients of parameters of the generative network fθ with respect to an aggregation of the loss array assembled in executions of (xi);
(xiv) optimize the parameters of the generative network fθ based on the results of (xiii), to provide an optimized generative network fθ, and
(xv) repeat (i) to (xiv) for each member of the set of training images.