US 12,001,520 B2
Generating simulated images that enhance socio-demographic diversity
Ritwik Sinha, Cupertino, CA (US); Sridhar Mahadevan, Morgan Hill, CA (US); Moumita Sinha, Cupertino, CA (US); Md Mehrab Tanjim, San Diego, CA (US); Krishna Kumar Singh, San Jose, CA (US); and David Arbour, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Sep. 27, 2021, as Appl. No. 17/485,780.
Prior Publication US 2023/0094954 A1, Mar. 30, 2023
Int. Cl. G06K 9/00 (2022.01); G06F 18/214 (2023.01); G06F 18/28 (2023.01); G06N 3/045 (2023.01)
CPC G06F 18/28 (2023.01) [G06F 18/2148 (2023.01); G06N 3/045 (2023.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method of generating simulated images that enhance socio-demographic diversity, the method comprising:
receiving, by an image-generating application, a request to generate a simulated image of a subject, wherein the request includes a set of target socio-demographic attributes that define one or more visual characteristics of the subject;
applying, by the image-generating application, a machine-learning model to the set of target socio-demographic attributes to generate the simulated image, wherein applying the machine-learning model includes:
processing, by a generator component of the machine-learning model, an embedding representing the set of target socio-demographic attributes to generate a candidate simulated image depicting a candidate subject represented by one or more candidate visual characteristics;
determining, by a discriminator component of the machine-learning model, that the one or more candidate visual characteristics of the candidate simulated image depict one or more anatomical features;
generating, by the discriminator component, a set of predicted sociodemographic attributes that define the one or more candidate visual characteristics;
determining, by the discriminator component, whether the set of predicted socio-demographic attributes substantially match the set of target socio-demographic attributes; and
in response to determining that the set of predicted socio-demographic attributes substantially match the set of target socio-demographic attributes, classifying, by the discriminator component, the candidate simulated image as the simulated image; and
outputting, by the image-generating application, the simulated image.