Invention Grant
- Patent Title: Synthesizing hard-negative text training data
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Application No.: US17127918Application Date: 2020-12-18
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Publication No.: US11948382B2Publication Date: 2024-04-02
- Inventor: Ophir Azulai , Udi Barzelay
- 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 Kristofer Haggerty
- Main IPC: G06V30/413
- IPC: G06V30/413 ; G06F18/21 ; G06F18/211 ; G06F18/241 ; G06F40/166 ; G06N20/00 ; G06V30/19

Abstract:
A method for synthesizing negative training data associated with training models to detect text within documents and images. The method includes one or more computer processors receiving a set of dictates associated with generating one or more negative training datasets for training a set of models to classify a plurality of features found within a data source. The method further includes identifying a set of rules related to generating negative training data to detect text based on the received set of dictates. The method further includes compiling one or more arrays of elements of hard-negative training data into a negative training data dataset based on the identified set of rules and one or more dictates. The method further includes determining metadata corresponding an array of elements of hard-negative training data.
Public/Granted literature
- US20220198186A1 SYNTHESIZING HARD-NEGATIVE TEXT TRAINING DATA Public/Granted day:2022-06-23
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