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    FEDERATED LEARNING USING SECURE CENTERS OF CLIENT DEVICE EMBEDDINGS
    2.
    发明申请
    FEDERATED LEARNING USING SECURE CENTERS OF CLIENT DEVICE EMBEDDINGS 有权

    公开(公告)号：US20220383197A1

    公开(公告)日：2022-12-01

    申请号：US17828613

    申请日：2022-05-31

    Applicant: QUALCOMM Incorporated

    Inventor： Hyunsin PARK ,  Hossein HOSSEINI ,  Sungrack YUN ,  Kyu Woong HWANG

    IPC: G06N20/00 ,  G06F21/62

    Abstract: Certain aspects of the present disclosure provide techniques for training a machine learning model. The method generally includes receiving, at a local device from a server, information defining a global version of a machine learning model. A local version of the machine learning model and a local center associated with the local version of the machine learning model are generated based on embeddings generated from local data at a client device and the global version of the machine learning model. A secure center different from the local center is generated based, at least in part, on information about secure centers shared by a plurality of other devices participating in a federated learning scheme. Information about the local version of the machine learning model and information about the secure center is transmitted by the local device to the server.

    SEMANTIC-AWARE RANDOM STYLE AGGREGATION FOR SINGLE DOMAIN GENERALIZATION
    3.
    发明公开
    SEMANTIC-AWARE RANDOM STYLE AGGREGATION FOR SINGLE DOMAIN GENERALIZATION 审中-公开

    公开(公告)号：US20230376753A1

    公开(公告)日：2023-11-23

    申请号：US18157723

    申请日：2023-01-20

    Applicant: QUALCOMM Incorporated

    Inventor： Seokeon CHOI ,  Sungha CHOI ,  Seunghan YANG ,  Hyunsin PARK ,  Debasmit DAS ,  Sungrack YUN

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems and techniques are provided for training a neural network model or machine learning model. For example, a method of augmenting training data can include augmenting, based on a randomly initialized neural network, training data to generate augmented training data and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data. The method can further include applying semantic-aware style fusion to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training the neural network model or machine learning model.

    MODEL COMPRESSION USING PRUNING QUANTIZATION AND KNOWLEDGE DISTILLATION
    4.
    发明申请
    MODEL COMPRESSION USING PRUNING QUANTIZATION AND KNOWLEDGE DISTILLATION 有权

    公开(公告)号：US20220318633A1

    公开(公告)日：2022-10-06

    申请号：US17705248

    申请日：2022-03-25

    Applicant: QUALCOMM Incorporated

    Inventor： Jangho KIM ,  Simyung CHANG ,  Hyunsin PARK ,  Juntae LEE ,  Jaewon CHOI ,  Kyu Woong HWANG

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A processor-implemented method for compressing a deep neural network model includes receiving an initial neural network model. The initial neural network is pruned based on a first threshold to generate a pruned network and a set of pruned weights. A quantization process is applied to the pruned network to produce a pruned and quantized network. A teacher model is generated by incorporating the pruned set of weights with the pruned network. In addition, an initial student model is generated from the quantized and pruned network. The initial student model is trained using the teacher model to output a trained student model.

    PERSONALIZED NEURAL NETWORK PRUNING
    5.
    发明申请
    PERSONALIZED NEURAL NETWORK PRUNING 有权

    公开(公告)号：US20220121949A1

    公开(公告)日：2022-04-21

    申请号：US17506646

    申请日：2021-10-20

    Applicant: QUALCOMM Incorporated

    Inventor： Simyung CHANG ,  Jangho KIM ,  Hyunsin PARK ,  Juntae LEE ,  Jaewon CHOI ,  Kyu Woong HWANG

    IPC: G06N3/08 ,  G06K9/62

    Abstract: A method for generating a personalized model includes receiving one or more personal data samples from a user. A prototype of a personal identity is generated based on the personal data samples. The prototype of the personal identity is trained to reflect personal characteristics of the user. A network graph is generated based on the prototype of the personal identity. One or more channels of a global network are pruned based on the network graph to produce the personalized model.

    CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION
    6.
    发明公开
    CLIENT-AGNOSTIC LEARNING AND ZERO-SHOT ADAPTATION FOR FEDERATED DOMAIN GENERALIZATION 审中-公开

    公开(公告)号：US20240112039A1

    公开(公告)日：2024-04-04

    申请号：US18238998

    申请日：2023-08-28

    Applicant: QUALCOMM Incorporated

    Inventor： Seunghan YANG ,  Seokeon CHOI ,  Hyunsin PARK ,  Sungha CHOI ,  Sungrack YUN

    IPC: G06N3/098 ,  H04L67/10

    CPC classification number: G06N3/098 ,  H04L67/10

    Abstract: Example implementations include methods, apparatuses, and computer-readable mediums of federated learning by a federated client device, comprising identifying client invariant information of a neural network for performing a machine learning (ML) task in a first domain known to a federated server. The implementations further comprising transmitting the client invariant information to the federated server, the federated server configured to generate a ML model for performing the ML task in a domain unknown to the federated server based on the client invariant information and other client invariant information of another neural network for performing the ML task in a second domain known to the federated server.

    SYSTEMS AND METHODS OF IMAGE PROCESSING BASED ON GAZE DETECTION
    7.
    发明公开
    SYSTEMS AND METHODS OF IMAGE PROCESSING BASED ON GAZE DETECTION 审中-公开

    公开(公告)号：US20230281885A1

    公开(公告)日：2023-09-07

    申请号：US17685278

    申请日：2022-03-02

    Applicant: QUALCOMM Incorporated

    Inventor： Hyunsin PARK ,  Juntae LEE ,  Simyung CHANG ,  Byeonggeun KIM ,  Jaewon CHOI ,  Kyu Woong HWANG

    IPC: G06T11/00 ,  G06V40/18 ,  G06F3/01 ,  G06V40/16

    CPC classification number: G06T11/00 ,  G06F3/013 ,  G06V40/174 ,  G06V40/18

    Abstract: Imaging systems and techniques are described. An imaging system receives image data representing at least a portion (e.g., a face) of a first user as captured by a first image sensor. The imaging system identifies that a gaze of the first user as represented in the image data is directed toward a displayed representation of at least a portion (e.g., a face) of a second user. The imaging system identifies an arrangement of representations of users for output. The imaging system generates modified image data based on the gaze and the arrangement at least in part by modifying the image data to modify at least the portion of the first user in the image data to be visually directed toward a direction corresponding to the second user based on the gaze and the arrangement. The imaging system outputs the modified image data arranged according to the arrangement.

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