MACHINE LEARNING DEVICE, METHOD, PROGRAM, AND SYSTEM

    公开(公告)号:US20220391759A1

    公开(公告)日:2022-12-08

    申请号:US17756002

    申请日:2020-11-12

    Abstract: The present invention is designed to improve the accuracy of machine learning. The present invention provides: a first classification unit configured to classify data into classifiable data and initially-unclassifiable data based on a first learning model; a first annotation unit configured to annotate the classifiable data with a label; a second classification unit configured to classify the initially-unclassifiable data based on a second learning model; a label acquiring unit configured to acquire a label with which the initially-unclassifiable data is to be annotated; a second annotation unit configured to annotate the initially-unclassifiable data with a label; and a second learning model updating unit configured to update the second learning model based on the initially-unclassifiable data that is annotated with the label based on a result of classification by the second classification unit and the label acquired by the label acquiring unit.

    IMAGE ANALYSIS APPARATUS, METHOD, AND PROGRAM

    公开(公告)号:US20220327675A1

    公开(公告)日:2022-10-13

    申请号:US17595608

    申请日:2020-04-24

    Abstract: To improve a determination accuracy when determining each particle contained in an image of an object. An image analysis apparatus according to an embodiment of the present invention includes: a shape determination unit configured to determine a shape of a particle included in a particle image that is extracted from an image of an object, so that an OK particle image which is a particle image of an OK particle that satisfies a predetermined standard for shape and a provisional NG particle image which is a particle image of a provisional NG particle that does not satisfy the predetermined standard, are obtained; a pseudo image generation unit configured to generate a pseudo image; and a similarity determination unit configured to determine whether the provisional NG image and the pseudo image are similar.

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