TRAINING AND APPLICATION METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND STORAGE MEDIUM

    公开(公告)号:US20240020519A1

    公开(公告)日:2024-01-18

    申请号:US18351417

    申请日:2023-07-12

    CPC classification number: G06N3/0495 G06N3/084

    Abstract: The present disclosure provides training and application methods and apparatuses for a neural network model, and a storage medium. The training method includes: quantizing, in a forward transfer process, a network parameter represented by a continuous real value, and calculating a quantization error; determining, in a backward transfer process, a gradient of a weight in the neural network model; correcting the gradient of the weight based on the calculated quantization error, wherein the correcting includes correcting a magnitude of the gradient and correcting a direction of the gradient; and updating the neural network model according to the corrected gradient.

    APPARATUS FOR PERFORMING FILTER PROCESSING USING CONVOLUTION OPERATION, METHOD OF PERFORMING FILTER PROCESSING, AND MEDIUM

    公开(公告)号:US20230154174A1

    公开(公告)日:2023-05-18

    申请号:US18055603

    申请日:2022-11-15

    CPC classification number: G06V10/82 G06F7/5443

    Abstract: An apparatus for performing filter processing on a data array in a processing target block of a predetermined size is provided. A data memory holds the data array in the processing target block. A coefficient memory holds weight coefficients of a filter used for the filter processing. A controller determines, in a determination, whether data in a reference region in the processing target block, set in correspondence with the processing target block, are zero values. A processor generates a convolution operation result of the weight coefficients and data at a plurality of positions in the processing target block. The controller controls, based on a result of the determination, whether to perform at least some of multiply-accumulate operations of the data and the weight coefficients when the processor generates the convolution operation result.

    INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20220392207A1

    公开(公告)日:2022-12-08

    申请号:US17825962

    申请日:2022-05-26

    Abstract: An information processing apparatus operable to perform computation processing in a neural network comprises a coefficient storage unit configured to store filter coefficients of the neural network, a feature storage unit configured to store feature data, a storage control unit configured to store in the coefficient storage unit a part of previously obtained feature data as template feature data, a convolution operation unit configured to compute new feature data by a convolution operation between feature data stored in the feature storage unit and filter coefficients stored in the coefficient storage unit, and compute, by a convolution operation between feature data stored in the feature storage unit and the template feature data stored in the coefficient storage unit, correlation data between the feature data stored in the feature storage unit and the template feature data.

    PROCESSING APPARATUS
    4.
    发明申请

    公开(公告)号:US20220012856A1

    公开(公告)日:2022-01-13

    申请号:US17353959

    申请日:2021-06-22

    Abstract: There is provided with a processing apparatus. A data holder holds at least some of data of a plurality of channels in a target layer among a plurality of layers. Each of a plurality of processors performs, in parallel, a product-sum operation using the data of one channel of the target layer and a coefficient corresponding to the target layer. A selector selects whether to perform first processing or second processing on the basis of information specifying processing in the target layer. The first processing includes inputting the data of one channel of the target layer into one of the plurality of processors. The second processing includes inputting the data of one channel of the target layer to the plurality of processors in parallel.

    IMAGE PROCESSING APPARATUS AND METHOD AND MONITORING SYSTEM

    公开(公告)号:US20190102887A1

    公开(公告)日:2019-04-04

    申请号:US16140279

    申请日:2018-09-24

    Abstract: An image processing apparatus including a unit configured to acquire a current image from an inputted video and a background model which comprises a background image and foreground/background classification information of visual elements; a unit configured to determine first similarity measures between visual elements in the current image and the visual elements in the background model; and a unit configured to classify the visual elements in the current image as the foreground or the background according to the current image, the background image in the background model and the first similarity measures. Wherein, the visual elements in the background model are the visual elements whose classification information is the background and which neighbour to corresponding portions of the visual elements in the current image. Accordingly, the accuracy of the foreground detection could be improved.

    Information processing apparatus, information processing method, and non-transitory computer readable storage medium

    公开(公告)号:US09824301B2

    公开(公告)日:2017-11-21

    申请号:US15139603

    申请日:2016-04-27

    Inventor: Tsewei Chen

    CPC classification number: G06K9/6282 G06K9/00228 G06K9/6257 G06K9/6286

    Abstract: In an information processing apparatus that includes sequences of weak classifiers which are logically cascade-connected in each sequence and the sequences respectively correspond to categories of an object and in which the weak classifiers are grouped into at least a first group and a second group in the order of connection, classification processing by weak classifiers belonging to the first group of respective categories is performed by pipeline processing. Based on the processing results of the weak classifiers belonging to the first group of the respective categories, categories in which classification processing by weak classifiers belonging to the second group is to be performed are decided out of the categories. The classification processing by the weak classifiers respectively corresponding to the decided categories and belonging to the second group is performed by pipeline processing.

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