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

Claim 19:
20. 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 including the steps of:
(i) the image generative network fθ generating an output image {circumflex over (x)} from an input image x of the set of training images, without tracking gradients for fθ;
(ii) the proxy network outputting an approximated function output ŷ, using x and {circumflex over (x)} as inputs;
(iii) the gradient intractable perceptual metric outputting a function output y, using x and {circumflex over (x)} as inputs;
(iv) evaluating a loss for the proxy network, using y and ŷ as inputs;
(v) using backpropagation to compute gradients of parameters of the proxy network with respect to the loss evaluated in step (iv);
(vi) optimizing the parameters of the proxy network based on the results of step (v), to provide an optimized proxy network;
(vii) the image generative network fθ generating an output image {circumflex over (x)} from an input image x,
(viii) the optimized proxy network outputting an optimized approximated function output ŷ, using x and {circumflex over (x)} as inputs;
(ix) evaluating a loss for the generative network fθ, using x, {circumflex over (x)} and the optimized approximated function output ŷ as inputs;
(x) using backpropagation to compute gradients of parameters of the generative network fθ with respect to the loss evaluated in step (ix);
(xi) optimizing the parameters of the generative network fθ based on the results of step (x), to provide an optimized generative network fθ, and
(xii) repeating steps (i) to (xi) for each member of the set of training images.