MODEL LEARNING DEVICE, MODEL LEARNING METHOD, AND PROGRAM

    公开(公告)号:US20210216818A1

    公开(公告)日:2021-07-15

    申请号:US17058099

    申请日:2019-05-28

    Abstract: Simultaneous learning of a plurality of different tasks and domains, with low costs and high precision, is enabled. A learning unit 160, on the basis of learning data, uses a target encoder that takes data of a target domain as input and outputs a target feature expression, a source encoder that takes data of a source domain as input and outputs a source feature expression, a common encoder that takes data of the target domain or the source domain as input and outputs a common feature expression, a target decoder that takes output of the target encoder and the common encoder as input and outputs a result of executing a task with regard to data of the target domain, and a source decoder that takes output of the source encoder and the common encoder as input and outputs a result of executing a task with regard to data of the source domain, to learn so that the output of the target decoder matches training data, and the output of the source decoder matches training data.

    LEARNING APPARATUS, ESTIMATION APPARATUS, LEARNING METHOD, ESTIMATION METHOD AND PROGRAM

    公开(公告)号:US20240005655A1

    公开(公告)日:2024-01-04

    申请号:US18247493

    申请日:2020-10-21

    CPC classification number: G06V10/98 G06V10/774 G06V10/764 G06V10/82

    Abstract: A learning apparatus includes: a data generation unit that learns generation of data based on a class label signal and a noise signal; an unknown degree estimation unit that learns estimation of a degree to which input data is unknown using a training set and the data generated by the data generation unit; a first class likelihood estimation unit that learns estimation of a first likelihood of each class label for input data using the training set; a second class likelihood estimation unit that learns estimation of a second likelihood of each class label for input data using the training set and the data generated by the data generation unit; a class likelihood correction unit that generates a third likelihood by correcting the first likelihood on the basis of the unknown degree and the second likelihood; and a class label estimation unit that estimates a class label of data related to the third likelihood on the basis of the third likelihood, thereby automatically estimating a cause of an error by a deep model.

    CLASS LABEL ESTIMATION APPARATUS, ERROR CAUSE ESTIMATION METHOD AND PROGRAM

    公开(公告)号:US20240281711A1

    公开(公告)日:2024-08-22

    申请号:US18548148

    申请日:2021-03-23

    CPC classification number: G06N20/00

    Abstract: There is provided a class label estimation device that estimates a class label of input data and estimates a cause of an estimation error, the class label estimation device including: a distribution estimation unit that estimates a distribution followed by a training set; a distance estimation unit that estimates a distance of the input data from the training set based on the distribution; an unknown degree estimation unit that estimates an unknown degree of the input data based on the distance; an unknown degree correction unit that corrects the unknown degree based on the distribution; and an error cause estimation unit that estimates a cause of an estimation error using the corrected unknown degree.

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