Method and apparatus for training a neural network used for denoising

    公开(公告)号:US11062210B2

    公开(公告)日:2021-07-13

    申请号:US16589620

    申请日:2019-10-01

    Abstract: A method, apparatus and computer program product provide an automated neural network training mechanism. The method, apparatus and computer program product receive a decoded noisy image and a set of input parameters for a neural network configured to optimize the decoded noisy image. A denoised image is generated based on the decoded noisy image and the set of input parameters. A denoised noisy error is computed representing an error between the denoised image and the decoded noisy image. The neural network is trained using the denoised noisy error and the set of input parameters and a ground truth noisy error value is received representing an error between the original image and the encoded image. The ground truth noisy error value is compared with the denoised noisy error to determine whether a difference between the ground truth noisy error value and the denoised noisy error is within a pre-determined threshold.

    Reverse neural network for object re-identification

    公开(公告)号:US20190122072A1

    公开(公告)日:2019-04-25

    申请号:US16156928

    申请日:2018-10-10

    Abstract: The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.

    Content-specific neural network distribution

    公开(公告)号:US11657264B2

    公开(公告)日:2023-05-23

    申请号:US15948304

    申请日:2018-04-09

    CPC classification number: G06N3/08 G06F7/588 H04L65/70

    Abstract: Media content is received for streaming to a user device. A neural network is trained based on a first portion of the media content. Weights of the neural network are updated to overfit the first portion of the media content to provide a first overfitted neural network. The neural network or the first overfitted neural network is trained based on a second portion of the media content. Weights of the neural network or the first overfitted neural network are updated to overfit the second portion of the media content to provide a second overfitted neural network. The first portion and the second portion of the media content are sent with associations to the first overfitted neural network and the second overfitted to the user equipment.

    Apparatus, a method and a computer program for video coding and decoding

    公开(公告)号:US11831867B2

    公开(公告)日:2023-11-28

    申请号:US17430893

    申请日:2020-01-29

    Abstract: A method comprising: obtaining a configuration of at least one neural network comprising a plurality of intra-prediction mode agnostic layers and one or more intra-prediction mode specific layers, the one or more intra-prediction mode specific layers corresponding to different intra-prediction modes; obtaining at least one input video frame comprising a plurality of blocks; determining to encode one or more blocks using intra prediction; determining an intra-prediction mode for each of said one or more blocks; grouping blocks having same intra-prediction mode into groups, each group being assigned with a computation path among the plurality of intra-prediction mode agnostic and the one or more intra-prediction mode specific layers; training the plurality of intra-prediction mode agnostic and/or the one or more intra-prediction mode specific layers of the neural networks based on a training loss between an output of the neural networks relating to a group of blocks and ground-truth blocks, wherein the ground-truth blocks are either blocks of the input video frame or reconstructed blocks; and encoding a block using a computation path assigned to an intra-prediction mode for the block.

    Method for analysing media content to generate reconstructed media content

    公开(公告)号:US11068722B2

    公开(公告)日:2021-07-20

    申请号:US16342084

    申请日:2017-09-27

    Abstract: The invention relates to a method, an apparatus and a computer program product for analyzing media content. The method comprises receiving media content; performing feature extraction of the media content at a plurality of convolution layers to produce a plurality of layer-specific feature maps; transmitting from the plurality of convolution layers a corresponding layer-specific feature map to a corresponding de-convolution layer of a plurality of de-convolution layers via a recurrent connection between the plurality of convolution layers and the plurality of de-convolution layers; and generating a reconstructed media content based on the plurality of feature maps.

    Content-Specific Neural Network Distribution

    公开(公告)号:US20190311259A1

    公开(公告)日:2019-10-10

    申请号:US15948304

    申请日:2018-04-09

    Abstract: According to the present disclosure, an apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to receive media content for streaming to a user device; to train a neural network to be overfitted to at least a first portion of the media content; and to send the trained neural network and the first portion of the media content to the user equipment. In addition, another apparatus includes at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to receive at least a first portion of media content and a neural network trained to be overfitted to the first portion of the media content; and to process the first portion of the media content using the overfitted neural network.

    Reverse neural network for object re-identification

    公开(公告)号:US11188783B2

    公开(公告)日:2021-11-30

    申请号:US16156928

    申请日:2018-10-10

    Abstract: The invention relates to a method comprising receiving, by a neural network, a first image comprising at least one target object; receiving, by the neural network, a second image comprising at least one query object; and determining, by the neural network, whether the query object corresponds to the target object, wherein the neural network comprises a discriminator neural network of a generative adversarial network (GAN). The invention further relates to an apparatus and a computer program product that perform the method.

    Method and an apparatus for evaluating generative machine learning model

    公开(公告)号:US10891524B2

    公开(公告)日:2021-01-12

    申请号:US16017742

    申请日:2018-06-25

    Abstract: The invention relates to a method comprising receiving a set of input samples, said set of input images comprising real images and generated images; extracting a set of feature maps from multiple layers of a pre-trained neural network for both the real images and the generated images; determining statistics for each feature map of the set of feature maps; comparing statistics of the feature maps for the real images to statistics of the feature maps for the generated images by using a distance function to obtain a vector of distances; and averaging the distances of the vector of distances to have a value indicating a diversity of the generated images. The invention also relates to technical equipment for implementing the method.

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