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    METHOD FOR OPTIMIZING ON-DEVICE NEURAL NETWORK MODEL BY USING SUB-KERNEL SEARCHING MODULE AND DEVICE USING THE SAME
    2.
    发明公开
    METHOD FOR OPTIMIZING ON-DEVICE NEURAL NETWORK MODEL BY USING SUB-KERNEL SEARCHING MODULE AND DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP3944154A1

    公开(公告)日：2022-01-26

    申请号：EP21153231.2

    申请日：2021-01-25

    申请人： Stradvision, Inc.

    发明人： GWEON, Sung An ,  KIM, Yongjoong ,  KANG, Bongnam ,  JE, Hongmo

    IPC分类号： G06N3/04 ,  G06N3/08

    摘要： A method for optimizing an on-device neural network model by using a Sub-kernel Searching Module is provided. The method includes steps of a learning device (a) if a Big Neural Network Model having a capacity capable of performing a targeted task by using a maximal computing power of an edge device has been trained to generate a first inference result on an input data, allowing the Sub-kernel Searching Module to identify constraint and a state vector corresponding to the training data, to generate architecture information on a specific sub-kernel suitable for performing the targeted task on the training data, (b) optimizing the Big Neural Network Model according to the architecture information to generate a specific Small Neural Network Model for generating a second inference result on the training data, and (c) training the Sub-kernel Searching Module by using the first and the second inference result.

    METHOD FOR PERFORMING ADJUSTABLE CONTINUAL LEARNING ON DEEP NEURAL NETWORK MODEL BY USING SELECTIVE DEEP GENERATIVE REPLAY MODULE AND DEVICE USING THE SAME
    3.
    发明公开
    METHOD FOR PERFORMING ADJUSTABLE CONTINUAL LEARNING ON DEEP NEURAL NETWORK MODEL BY USING SELECTIVE DEEP GENERATIVE REPLAY MODULE AND DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP3913541A1

    公开(公告)日：2021-11-24

    申请号：EP21158573.2

    申请日：2021-02-23

    申请人： Stradvision, Inc.

    发明人： BOO, SukHoon ,  GWEON, SungAn ,  KIM, Yongjoong ,  RYU, Wooju

    IPC分类号： G06N3/04 ,  G06N3/08

    摘要： A method of adjustable continual learning of a deep neural network model by using a selective deep generative replay module is provided. The method includes steps of: a learning device (a) (i) inputting training data from a total database and a sub-database into the selective deep generative replay module to generate first and second low-dimensional distribution features, (ii) inputting binary values, random parameters, and the second low-dimensional distribution features into a data generator to generate a third training data, and (ii) inputting a first training data into a solver to generate labeled training data; (b) inputting the training data, the low-dimensional distribution features, and the binary values into a discriminator to generate a first and a second training data scores, a first and a second feature distribution scores, and a third training data score; and (c) training the discriminator, the data generator, the distribution analyzer and the solver.

    METHOD FOR PERFORMING ON-DEVICE LEARNING OF MACHINE LEARNING NETWORK ON AUTONOMOUS VEHICLE BY USING MULTI-STAGE LEARNING WITH ADAPTIVE HYPER-PARAMETER SETS AND DEVICE USING THE SAME
    4.
    发明公开
    METHOD FOR PERFORMING ON-DEVICE LEARNING OF MACHINE LEARNING NETWORK ON AUTONOMOUS VEHICLE BY USING MULTI-STAGE LEARNING WITH ADAPTIVE HYPER-PARAMETER SETS AND DEVICE USING THE SAME 审中-公开

    公开(公告)号：EP3910563A1

    公开(公告)日：2021-11-17

    申请号：EP21171875.4

    申请日：2021-05-03

    申请人： Stradvision, Inc.

    发明人： JE, Hongmo ,  KIM, Yongjoong ,  YU, Dongkyu ,  GWEON, SungAn

    IPC分类号： G06N20/00

    摘要： A method for performing on-device learning of embedded machine learning network of autonomous vehicle by using multi-stage learning with adaptive hyper-parameter sets is provided. The processes include: (a) dividing the current learning into a 1-st stage learning to an n-th stage learning, assigning 1-st stage training data to n-th stage training data, generating a 1_1-st hyper-parameter set candidate to a 1_h-th hyper-parameter set candidate, training the embedded machine learning network in the 1-st stage learning, and determining a 1-st adaptive hyper-parameter set; (b) generating a k_1-st hyper-parameter set candidate to a k_h-th hyper-parameter set candidate, training the (k-1)-th stage-completed machine learning network in the k-th stage learning, and determining a k-th adaptive hyper-parameter set; and (c) generating an n-th adaptive hyper-parameter set, and executing the n-th stage learning, to thereby complete the current learning.