SPARSE BINARY REPRESENTATION FOR SELF SUPERVISED INFORMATION EXTRACTION

    公开(公告)号:US20230297815A1

    公开(公告)日:2023-09-21

    申请号:US18184616

    申请日:2023-03-15

    CPC classification number: G06N3/0455 G06N3/0985

    Abstract: A method for generating a sparse binary representation (SBR) of neural network intermediate features (NNIFs) of a neural network (NN). The method includes (i) feeding the neural network by input information; (ii) neural network processing the input information to provide, at least, the NNIFs; (iii) SBR processing, by a SBR module, the NNIFs, to provide the SBR representation of the NNIFs; and (iv) outputting the SBR representation. The SBR module has undergone a training process that used a loss function that takes into account a sparsity of training process SBR representations.

    PASSIVE READOUT
    2.
    发明公开
    PASSIVE READOUT 审中-公开

    公开(公告)号:US20240005152A1

    公开(公告)日:2024-01-04

    申请号:US18327865

    申请日:2023-06-01

    CPC classification number: G06N3/08

    Abstract: A method for passive readout, the method may include (i) obtaining a group of descriptors that were outputted by of one or more neural network layers; wherein descriptors of the group of descriptors comprise a first number (N1) of descriptor elements; and (ii) generating a lossless and sparse representation of the group of descriptors. The generating may include (a) applying a dimension expanding convolution operation on the group of descriptors to provide a group of expanded descriptors; wherein expanded descriptors of the group of expanded descriptors comprises a second number (N2) of expanded descriptor elements, wherein N2 exceeds N1; and (b) quantizing the group of expanded descriptors to provide a group of binary descriptors that form a lossless and a sparse representation of the group of descriptors.

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