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    METHODS, DEVICES AND MEDIA PROVIDING AN INTEGRATED TEACHER-STUDENT SYSTEM
    4.
    发明申请
    METHODS, DEVICES AND MEDIA PROVIDING AN INTEGRATED TEACHER-STUDENT SYSTEM 有权

    公开(公告)号：US20210279595A1

    公开(公告)日：2021-09-09

    申请号：US16810524

    申请日：2020-03-05

    申请人： Deepak SRIDHAR ,  Juwei LU

    发明人： Deepak SRIDHAR ,  Juwei LU

    IPC分类号： G06N3/08 ,  G06N3/04

    摘要： Methods, devices and processor-readable media for an integrated teacher-student machine learning system. One or more teacher-student modules are trained as part of the teacher neural network training. Each student sub-network uses a portion of the teacher neural network to generate an intermediate feature map, then provides the intermediate feature map to a student sub-network to generate inferences. The student sub-network may use a feature enhancement block to map the intermediate feature map to a subsequent feature map. A compression block may be used to compress intermediate feature map data for transmission in some embodiments.

    METHODS AND SYSTEMS FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION
    7.
    发明申请
    METHODS AND SYSTEMS FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION 有权

    公开(公告)号：US20220300823A1

    公开(公告)日：2022-09-22

    申请号：US17204670

    申请日：2021-03-17

    申请人： Hanwen LIANG ,  Peng DAI ,  Qiong ZHANG ,  Juwei LU

    发明人： Hanwen LIANG ,  Peng DAI ,  Qiong ZHANG ,  Juwei LU

    IPC分类号： G06N3/08 ,  G06N3/04

    摘要： Methods, systems, and media for training deep neural networks for cross-domain few-shot classification are described. The methods comprise an encoder and a decoder of a deep neural network. The training of the autoencoder comprises two training stages. For each iteration in the first training stage, a batch of data samples from the source dataset are sampled and fed to the encoder to generate a plurality of source feature maps, then determining a first training stage loss, which updates the autoencoder's parameters. For each iteration in the second training stage, the novel dataset is split into a support set and a query set. The support set is fed to the encoder to determine a prototype for each class label. The query set is also fed to the encoder to calculate a query set metric classification loss. The query set metric classification loss updates the autoencoder's parameters.

    METHODS, SYSTEMS, AND MEDIA FOR IMAGE SEARCHING
    10.
    发明申请
    METHODS, SYSTEMS, AND MEDIA FOR IMAGE SEARCHING 有权

    公开(公告)号：US20220405322A1

    公开(公告)日：2022-12-22

    申请号：US17354786

    申请日：2021-06-22

    申请人： Varshanth RAO ,  Md Ibrahim KHALIL ,  Peng DAI ,  Juwei LU

    发明人： Varshanth RAO ,  Md Ibrahim KHALIL ,  Peng DAI ,  Juwei LU

    IPC分类号： G06F16/55 ,  G06F16/53 ,  G06F16/51 ,  G06F16/583 ,  G06K9/62

    摘要： Methods, systems, and media for image searching are described. Images comprising one query image and a plurality of candidate images are received. For each candidate image, a first model similarity measure from an output of a first model configured for scene classification to perceive scenes in the images is determined. Further, for each candidate image of the plurality of candidate images, a second model similarity measure from the output of a second model configured for attribute classification to perceive attributes in the images is determined. For each candidate image of the plurality of candidate images, a similarity agglomerate index of a weighted aggregate of the first model similarity measure and the second model similarity measure is computed. The plurality of candidate images based on the respective similarity agglomerate index of each candidate image are ranked and a first ranked candidate images corresponding to the searched images are generated.