Invention Application
- Patent Title: Self-Supervised Learning for Anomaly Detection and Localization
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Application No.: US17454605Application Date: 2021-11-11
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Publication No.: US20220156521A1Publication Date: 2022-05-19
- Inventor: Kihyuk Sohn , Chun-Liang Li , Jinsung Yoon , Tomas Jon Pfister
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06K9/20 ; G06N3/08

Abstract:
A method for training a machine learning model includes obtaining a set of training samples. For each training sample in the set of training samples, during each of one or more training iterations, the method includes cropping the training sample to generate a first cropped image, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. The first portion is different than the second portion. The method also includes training the machine learning model with the first cropped image and the augmented second cropped image.
Public/Granted literature
- US11941084B2 Self-supervised learning for anomaly detection and localization Public/Granted day:2024-03-26
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