CPC G06N 3/08 (2013.01) [G06F 3/04845 (2013.01); G06F 3/04847 (2013.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06T 3/0006 (2013.01); G06T 3/0093 (2013.01); G06T 3/40 (2013.01); G06T 3/4038 (2013.01); G06T 3/4046 (2013.01); G06T 5/005 (2013.01); G06T 5/20 (2013.01); G06T 11/001 (2013.01); G06T 11/60 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2210/22 (2013.01)] | 20 Claims |
1. A computer-implemented method comprising:
providing, by a computing system, an input image as input to a machine learning model to generate a latent space representation of the input image;
providing, by the computing system, the latent space representation of the input image as input to a trained generator neural network implemented by the computing system;
generating, by the generator neural network, a generated image based on the latent space representation of the input image;
generating, by the computing system, a first scale representation of the input image and a second scale representation of the input image;
generating, by the computing system, a first scale representation of the generated image and a second scale representation of the generated image;
generating, by the computing system, a first combined image based on the first scale representation of the input image, the first scale representation of the generated image, and a first value;
generating, by the computing system, a second combined image based on the second scale representation of the input image, the second scale representation of the generated image, and a second value different from the first value; and
blending, by the computing system, the first combined image with the second combined image to generate an output image.
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