Patent ID: 11947632
Assignee: MAPLEBEAR INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:
obtain training data from a source domain, the training data comprising a plurality of examples, each example comprising an image with a label applied to each example identifying a class in the source domain for the example;
obtain a description of classes in a target domain different than the source domain, the classes in the target domain different from classes in the source domain;
for each class in the target domain, select a set of classes in the source domain corresponding to the class in the target domain;
generate a source domain training set comprising examples of the training data from the source domain, each example of the source domain training set comprising an image with a class label identifying the class in the target domain corresponding to the class in the source domain for the example;
obtaining a second training set comprising one or more additional examples comprising an image from the target domain or an image from the source domain with a domain label identifying a domain of an additional example;
training a classification model comprising a domain adversarial neural network configured to output a predicted class in the target domain for an input image by:
applying the classification model to examples of the source domain training set and modifying one or more parameters of the classification based on backpropagation of an error term from a difference between a predicted class in the target domain for an example of the source domain training set and a class label for the example of the source domain training set until the error term satisfies one or more conditions; and
applying the classification model to examples of the second training set to which the domain label is applied and modifying one or more parameters of the classification based on backpropagation of a domain error term from a difference between a predicted domain of the example of the second training set and the domain label applied to the example of the second training set satisfies one or more conditions;

apply the trained classification model to a selected image; and
store a predicted class in the target domain for the selected image output by the trained classification model in association with the selected image.