Invention Publication
- Patent Title: SYSTEMS AND METHODS FOR ESTIMATING ROBUSTNESS OF A MACHINE LEARNING MODEL
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Application No.: US17973177Application Date: 2022-10-24
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Publication No.: US20240135689A1Publication Date: 2024-04-25
- Inventor: Yuya SUGASAWA , Hisaji MURATA , Nway Nway AUNG , Ariel BECK , Zong Sheng TANG
- Applicant: Panasonic Intellectual Property Management Co., Ltd.
- Applicant Address: JP Osaka
- Assignee: Panasonic Intellectual Property Management Co., Ltd.
- Current Assignee: Panasonic Intellectual Property Management Co., Ltd.
- Current Assignee Address: JP Osaka
- Main IPC: G06V10/776
- IPC: G06V10/776

Abstract:
According to an embodiment, a method for estimating robustness of a trained machine learning model is disclosed. The method comprises receiving a labelled dataset, a model of an object for which defect detection is required, and the trained machine learning model. Further, the method comprises determining one or more parameters associated with image capturing conditions in the environment. Furthermore, the method comprises performing an auto extraction of one or more defects using the model of the object and the labelled dataset based on image processing. Furthermore, the method comprises generating one or more images based on the one or more parameters and the one or more defects. Additionally, the method comprises testing the trained machine learning model using the generated images. Moreover, the method comprises estimating a robustness report for the machine learning model based on the testing of the machine learning model.
Public/Granted literature
- US20240233344A9 SYSTEMS AND METHODS FOR ESTIMATING ROBUSTNESS OF A MACHINE LEARNING MODEL Public/Granted day:2024-07-11
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