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    METHODS AND SYSTEMS FOR BUDGETED AND SIMPLIFIED TRAINING OF DEEP NEURAL NETWORKS
    13.
    发明申请
    METHODS AND SYSTEMS FOR BUDGETED AND SIMPLIFIED TRAINING OF DEEP NEURAL NETWORKS 有权

    公开(公告)号：US20220222492A1

    公开(公告)日：2022-07-14

    申请号：US17584216

    申请日：2022-01-25

    Applicant: Intel Corporation

    Inventor： Yiwen GUO ,  Yuqing Hou ,  Anbang Yao ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yurong Chen ,  Libin Wang

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/94

    Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

    Methods and systems for budgeted and simplified training of deep neural networks
    14.
    发明授权
    Methods and systems for budgeted and simplified training of deep neural networks 有权

    公开(公告)号：US11263490B2

    公开(公告)日：2022-03-01

    申请号：US16475078

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

    Inventor： Yiwen Guo ,  Yuqing Hou ,  Anbang Yao ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shandong Wang ,  Wenhua Cheng ,  Yurong Chen ,  Libin Wang

    IPC: G06K9/62 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

    METHODS AND SYSTEMS FOR BOOSTING DEEP NEURAL NETWORKS FOR DEEP LEARNING
    19.
    发明申请
    METHODS AND SYSTEMS FOR BOOSTING DEEP NEURAL NETWORKS FOR DEEP LEARNING 审中-公开

    公开(公告)号：US20200026999A1

    公开(公告)日：2020-01-23

    申请号：US16475076

    申请日：2017-04-07

    Applicant: INTEL CORPORATION

    Inventor： Libin Wang ,  Yiwen Guo ,  Anbang Yao ,  Dongqi Cai ,  Lin Xu ,  Ping Hu ,  Shangong Wang ,  Wenhua Cheng ,  Yurong Chen

    IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G06T1/20 ,  G06K9/62

    Abstract: Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.

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