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

Claim 0:
1. A classification model stored on a non-transitory computer readable storage medium, wherein the classification model is manufactured by a process comprising:
obtaining 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;
for each class in a target domain, selecting a set of classes in the source domain corresponding to a class in the target domain, classes in the target domain different from classes in the source 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;
initializing a plurality of layers of a neural network, the plurality of layers including feature extractor layers configured to output a set of features that are input into each of a domain classifier set of layers and a class predictor set of layers and a gradient reversal layer coupling the feature extractor to the domain classifier, the gradient reversal layer configured to multiply data backpropagated from the domain classifier by a negative constant;
training the classification model by backpropagating by:
selecting the class predictor set of layers or the domain classifier set of layers;
responsive to selecting the class predictor set of layers, selecting an example from the source domain training set and backpropagating through the class predictor set of layers and feature extractor layers of the model using a loss function based on matching output of the class predictor to the class label of the selected example;
responsive to selecting the domain classifier set of layers, selecting an additional example from the second training set and backpropagating through the domain classifier set of layers, gradient reversal layer, and feature extractor layers of the model using a loss function based on matching output of the domain classifier to the domain label of the additional example;
stopping the backpropagating when one or more criteria are met; and

storing a set of parameters of the feature extractor layers and the class predictor set of layers of the network on the computer readable storage medium as parameters of the classification model.