Invention Grant
- Patent Title: Data labeling for deep-learning models
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Application No.: US16354352Application Date: 2019-03-15
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Publication No.: US10885332B2Publication Date: 2021-01-05
- Inventor: Rafal Bigaj , Lukasz G. Cmielowski , Marek Oszajec , Maksymilian Erazmus
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Daniel C. Housley
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06K9/00 ; G06N3/08

Abstract:
A first and second scoring endpoint with payload logging are deployed. At the second scoring endpoint, native data and a user-generated score for the native data are received, the native data is pre-processed into readable data for the deep-learning model, and the user-generated score and the readable data are output to the first scoring endpoint, which is associated directly with the deep-learning model. A raw payload that includes the native data is output to a payload store. At the first scoring endpoint, the readable data and the user-generated score are processed by the deep-learning model, which outputs a transformed payload and a prediction, respectively, to the payload store. The raw payload is matched with the transformed payload and the prediction to produce a comprehensive data set, which is evaluated to describe a set of transformation parameters. The deep-learning model is retrained to account for the set of transformation parameters.
Public/Granted literature
- US20200293774A1 DATA LABELING FOR DEEP-LEARNING MODELS Public/Granted day:2020-09-17
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