Invention Grant
- Patent Title: Using multiple trained models to reduce data labeling efforts
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Application No.: US17221661Application Date: 2021-04-02
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Publication No.: US11714802B2Publication Date: 2023-08-01
- Inventor: Matthew Shreve , Francisco E. Torres , Raja Bala , Robert R. Price , Pei Li
- Applicant: PALO ALTO RESEARCH CENTER INCORPORATED
- Applicant Address: US CA Palo Alto
- Assignee: Palo Alto Research Center Incorporated
- Current Assignee: Palo Alto Research Center Incorporated
- Current Assignee Address: US CA Palo Alto
- Agency: Womble Bond Dickinson (US) LLP
- Main IPC: G06F16/00
- IPC: G06F16/00 ; G06F16/23 ; G06N20/00

Abstract:
A method of labeling a dataset of input samples for a machine learning task includes selecting a plurality of pre-trained machine learning models that are related to a machine learning task. The method further includes processing a plurality of input data samples through each of the pre-trained models to generate a set of embeddings. The method further includes generating a plurality of clusterings from the set of embeddings. The method further includes analyzing, by a processing device, the plurality of clusterings to extract superclusters. The method further includes assigning pseudo-labels to the input samples based on analysis.
Public/Granted literature
- US20220318229A1 USING MULTIPLE TRAINED MODELS TO REDUCE DATA LABELING EFFORTS Public/Granted day:2022-10-06
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