CONVOLUTIONAL NEURAL NETWORK (CNN) PROCESSING METHOD AND APPARATUS

    公开(公告)号:US20230051648A1

    公开(公告)日:2023-02-16

    申请号:US17975837

    申请日:2022-10-28

    IPC分类号: G06N3/04 G06N3/08

    摘要: Disclosed is a convolutional neural network (CNN) processing apparatus and method, the apparatus configured to determine a loading space unit for at least one loading space in an input based on a height or a width for an input feature map of the input and an extent of a dimension of a kernel feature map, load target input elements corresponding to a target loading space, among the at least one loading space, from a memory and store the target input elements in an allocated input buffer having a size corresponding to the loading space unit, and perform a convolution operation between the target input elements stored in the input buffer and at least one kernel element of a kernel.

    NEURAL NETWORK METHOD AND APPARATUS

    公开(公告)号:US20220138576A1

    公开(公告)日:2022-05-05

    申请号:US17574408

    申请日:2022-01-12

    摘要: A lightened neural network method and apparatus. The neural network apparatus includes a processor configured to generate a neural network with a plurality of layers including plural nodes by applying lightened weighted connections between neighboring nodes in neighboring layers of the neural network to interpret input data applied to the neural network, wherein lightened weighted connections of at least one of the plurality of layers includes weighted connections that have values equal to zero for respective non-zero values whose absolute values are less than an absolute value of a non-zero value. The lightened weighted connections also include weighted connections that have values whose absolute values are no greater than an absolute value of another non-zero value, the lightened weighted connections being lightened weighted connections of trained final weighted connections of a trained neural network whose absolute maximum values are greater than the absolute value of the other non-zero value.

    FACE VERIFICATION METHOD AND APPARATUS

    公开(公告)号:US20210089755A1

    公开(公告)日:2021-03-25

    申请号:US17111912

    申请日:2020-12-04

    IPC分类号: G06K9/00 G06K9/78 G06K9/62

    摘要: Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.

    APPARATUS AND METHOD WITH USER VERIFICATION
    6.
    发明申请

    公开(公告)号:US20200210685A1

    公开(公告)日:2020-07-02

    申请号:US16728056

    申请日:2019-12-27

    IPC分类号: G06K9/00 G06F21/32

    摘要: A processor-implemented verification method includes: detecting a characteristic of an input image; acquiring input feature transformation data and enrolled feature transformation data by respectively transforming input feature data and enrolled feature data based on the detected characteristic, wherein the input feature data is extracted from the input image using a feature extraction model; and verifying a user corresponding to the input image based on a result of comparison between the input feature transformation data and the enrolled feature transformation data.

    USER VERIFICATION METHOD AND APPARATUS USING GENERALIZED USER MODEL

    公开(公告)号:US20200210556A1

    公开(公告)日:2020-07-02

    申请号:US16545095

    申请日:2019-08-20

    IPC分类号: G06F21/31 G06K9/62 G06N3/08

    摘要: A user verification method and apparatus using a generalized user model is disclosed, where the user verification method includes generating a feature vector corresponding to a user based on input data corresponding to the user, determining a first parameter indicating a similarity between the feature vector and an enrolled feature vector enrolled for user verification, determining a second parameter indicating a similarity between the feature vector and a user model corresponding to generalized users, and verifying the user based on the first parameter and the second parameter.