Invention Publication
- Patent Title: TRAINING A CLASSIFICATION MODEL USING LABELED TRAINING DATA THAT DOES NOT OVERLAP WITH TARGET CLASSIFICATIONS FOR THE CLASSIFICATION MODEL
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Application No.: US18436611Application Date: 2024-02-08
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Publication No.: US20240176852A1Publication Date: 2024-05-30
- Inventor: Saurav Manchanda , Krishnakumar Subramanian , Haixun Wang , Min Xie
- Applicant: Maplebear Inc.
- Applicant Address: US CA San Francisco
- Assignee: Maplebear Inc.
- Current Assignee: Maplebear Inc.
- Current Assignee Address: US CA San Francisco
- Main IPC: G06F18/2411
- IPC: G06F18/2411 ; G06F18/214 ; G06F18/22 ; G06N3/084

Abstract:
An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.
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