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    SYSTEM AND METHOD FOR DEEP MACHINE LEARNING FOR COMPUTER VISION APPLICATIONS
    81.
    发明申请
    SYSTEM AND METHOD FOR DEEP MACHINE LEARNING FOR COMPUTER VISION APPLICATIONS 有权

    公开(公告)号：US20220391632A1

    公开(公告)日：2022-12-08

    申请号：US17889883

    申请日：2022-08-17

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Haoyu Ren ,  Mostafa El-Khamy ,  Jungwon Lee ,  Aman Raj

    IPC: G06K9/62 ,  G06N20/00 ,  G06N7/00 ,  G06T7/593 ,  G06T7/11 ,  G06T15/00

    Abstract: A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

    System and method for acoustic echo cancelation using deep multitask recurrent neural networks
    82.
    发明授权
    System and method for acoustic echo cancelation using deep multitask recurrent neural networks 有权

    公开(公告)号：US11521634B2

    公开(公告)日：2022-12-06

    申请号：US17015931

    申请日：2020-09-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Amin Fazeli ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G10L21/0232 ,  H04R3/04 ,  G06N20/10 ,  G06N3/08 ,  G10L21/0208

    Abstract: A method for performing echo cancellation includes: receiving a far-end signal from a far-end device at a near-end device; recording a microphone signal at the near-end device including: a near-end signal; and an echo signal corresponding to the far-end signal; extracting far-end features from the far-end signal; extracting microphone features from the microphone signal; computing estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including: an echo estimator including a first stack of a recurrent neural network configured to compute estimated echo features based on the far-end features; and a near-end estimator including a second stack of the recurrent neural network configured to compute the estimated near-end features based on an output of the first stack and the microphone signal; computing an estimated near-end signal from the estimated near-end features; and transmitting the estimated near-end signal to the far-end device.

    System and method for boundary aware semantic segmentation
    83.
    发明授权
    System and method for boundary aware semantic segmentation 有权

    公开(公告)号：US11461998B2

    公开(公告)日：2022-10-04

    申请号：US16777734

    申请日：2020-01-30

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Qingfeng Liu ,  Mostafa El-Khamy ,  Dongwoon Bai ,  Jungwon Lee

    IPC: G06K9/36 ,  G06V10/48 ,  G06K9/62 ,  G06N3/08 ,  G06N20/00 ,  G06V10/75

    Abstract: Some aspects of embodiments of the present disclosure relate to using a boundary aware loss function to train a machine learning model for computing semantic segmentation maps from input images. Some aspects of embodiments of the present disclosure relate to deep convolutional neural networks (DCNNs) for computing semantic segmentation maps from input images, where the DCNNs include a box filtering layer configured to box filter input feature maps computed from the input images before supplying box filtered feature maps to an atrous spatial pyramidal pooling (ASPP) layer. Some aspects of embodiments of the present disclosure relate to a selective ASPP layer configured to weight the outputs of an ASPP layer in accordance with attention feature maps.

    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS
    84.
    发明申请
    SYSTEM AND METHOD FOR ACOUSTIC ECHO CANCELATION USING DEEP MULTITASK RECURRENT NEURAL NETWORKS 有权

    公开(公告)号：US20220293120A1

    公开(公告)日：2022-09-15

    申请号：US17827424

    申请日：2022-05-27

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Amin Fazeli ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G10L21/0232 ,  G06N3/08 ,  G06N20/10 ,  H04R3/04

    Abstract: A system for performing echo cancellation includes: a processor configured to: receive a far-end signal; record a microphone signal including: a near-end signal; and an echo signal corresponding to the far-end signal; extract far-end features from the far-end signal; extract microphone features from the microphone signal; compute estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including a recurrent neural network including: an encoder including a plurality of gated recurrent units; and a decoder including a plurality of gated recurrent units; compute an estimated near-end signal from the estimated near-end features; and transmit the estimated near-end signal to the far-end device. The recurrent neural network may include a contextual attention module; and the recurrent neural network may take, as input, a plurality of error features computed based on the far-end features, the microphone features, and acoustic path parameters.

    METHOD AND APPARATUS FOR INCREMENTAL LEARNING
    85.
    发明申请
    METHOD AND APPARATUS FOR INCREMENTAL LEARNING 有权

    公开(公告)号：US20220138633A1

    公开(公告)日：2022-05-05

    申请号：US17317421

    申请日：2021-05-11

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Yoo Jin Choi ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G06N20/20 ,  G06K9/62

    Abstract: An electronic device and method for performing class-incremental learning are provided. The method includes designating a pre-trained first model for at least one past data class as a first teacher; training a second model; designating the trained second model as a second teacher; performing dual-teacher information distillation by maximizing mutual information at intermediate layers of the first teacher and second teacher; and transferring the information to a combined student model.

    Method and apparatus for learning low-precision neural network that combines weight quantization and activation quantization
    86.
    发明授权
    Method and apparatus for learning low-precision neural network that combines weight quantization and activation quantization 有权

    公开(公告)号：US11270187B2

    公开(公告)日：2022-03-08

    申请号：US15914229

    申请日：2018-03-07

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Yoo Jin Choi ,  Mostafa El-Khamy ,  Jungwon Lee

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A method is provided. The method includes selecting a neural network model, wherein the neural network model includes a plurality of layers, and wherein each of the plurality of layers includes weights and activations; modifying the neural network model by inserting a plurality of quantization layers within the neural network model; associating a cost function with the modified neural network model, wherein the cost function includes a first coefficient corresponding to a first regularization term, and wherein an initial value of the first coefficient is pre-defined; and training the modified neural network model to generate quantized weights for a layer by increasing the first coefficient until all weights are quantized and the first coefficient satisfies a pre-defined threshold, further including optimizing a weight scaling factor for the quantized weights and an activation scaling factor for quantized activations, and wherein the quantized weights are quantized using the optimized weight scaling factor.

    SYSTEM AND METHOD FOR BOUNDARY AWARE SEMANTIC SEGMENTATION
    88.
    发明申请
    SYSTEM AND METHOD FOR BOUNDARY AWARE SEMANTIC SEGMENTATION 有权

    公开(公告)号：US20210089807A1

    公开(公告)日：2021-03-25

    申请号：US16777734

    申请日：2020-01-30

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Qingfeng Liu ,  Mostafa El-Khamy ,  Dongwoon Bai ,  Jungwon Lee

    IPC: G06K9/46 ,  G06N3/08 ,  G06K9/62 ,  G06N20/00

    Abstract: Some aspects of embodiments of the present disclosure relate to using a boundary aware loss function to train a machine learning model for computing semantic segmentation maps from input images. Some aspects of embodiments of the present disclosure relate to deep convolutional neural networks (DCNNs) for computing semantic segmentation maps from input images, where the DCNNs include a box filtering layer configured to box filter input feature maps computed from the input images before supplying box filtered feature maps to an atrous spatial pyramidal pooling (ASPP) layer. Some aspects of embodiments of the present disclosure relate to a selective ASPP layer configured to weight the outputs of an ASPP layer in accordance with attention feature maps.

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