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    Training neural networks using consistency measures
    21.
    发明授权
    Training neural networks using consistency measures 有权

    公开(公告)号：US11544498B2

    公开(公告)日：2023-01-03

    申请号：US17194090

    申请日：2021-03-05

    Applicant: Google LLC

    Inventor： Ariel Gordon ,  Soeren Pirk ,  Anelia Angelova ,  Vincent Michael Casser ,  Yao Lu ,  Anthony Brohan ,  Zhao Chen ,  Jan Dlabal

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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.

    Unsupervised learning of image depth and ego-motion prediction neural networks
    22.
    发明授权
    Unsupervised learning of image depth and ego-motion prediction neural networks 有权

    公开(公告)号：US10810752B2

    公开(公告)日：2020-10-20

    申请号：US16861441

    申请日：2020-04-29

    Applicant: Google LLC

    Inventor： Anelia Angelova ,  Martin Wicke ,  Reza Mahjourian

    IPC: G06K9/36 ,  G06T7/579 ,  G06N3/08 ,  G06T7/285 ,  G06N3/04

    Abstract: A system includes a neural network implemented by one or more computers, in which the neural network includes an image depth prediction neural network and a camera motion estimation neural network. The neural network is configured to receive a sequence of images. The neural network is configured to process each image in the sequence of images using the image depth prediction neural network to generate, for each image, a respective depth output that characterizes a depth of the image, and to process a subset of images in the sequence of images using the camera motion estimation neural network to generate a camera motion output that characterizes the motion of a camera between the images in the subset. The image depth prediction neural network and the camera motion estimation neural network have been jointly trained using an unsupervised learning technique.

    IMAGE DEPTH PREDICTION NEURAL NETWORKS
    23.
    发明申请
    IMAGE DEPTH PREDICTION NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190279383A1

    公开(公告)日：2019-09-12

    申请号：US16332991

    申请日：2017-09-12

    Applicant: Google LLC

    Inventor： Anelia Angelova ,  Martin Wicke ,  Reza Mahjourian

    IPC: G06T7/55 ,  G06T3/40

    Abstract: A system includes an image depth prediction neural network implemented by one or more computers. The image depth prediction neural network is a recurrent neural network that is configured to receive a sequence of images and, for each image in the sequence: process the image in accordance with a current internal state of the recurrent neural network to (i) update the current internal state and (ii) generate a depth output that characterizes a predicted depth of a future image in the sequence.

    Object recognition from videos using recurrent neural networks
    24.
    发明授权
    Object recognition from videos using recurrent neural networks 有权

    公开(公告)号：US10013640B1

    公开(公告)日：2018-07-03

    申请号：US14976147

    申请日：2015-12-21

    Applicant: Google LLC

    Inventor： Anelia Angelova ,  Ivan Bogun

    IPC: G06K9/62

    CPC classification number: G06K9/00624 ,  G06K9/4628 ,  G06K9/6271

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying an object from a video. One of the methods includes obtaining multiple frames from a video, where each frame of the multiple frames depicts an object to be recognized, and processing, using an object recognition model, the multiple frames to generate data that represents a classification of the object to be recognized.

    UNSUPERVISED DEPTH PREDICTION NEURAL NETWORKS
    26.
    发明公开
    UNSUPERVISED DEPTH PREDICTION NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230419521A1

    公开(公告)日：2023-12-28

    申请号：US18367888

    申请日：2023-09-13

    Applicant: Google LLC

    Inventor： Vincent Michael Casser ,  Soeren Pirk ,  Reza Mahjourian ,  Anelia Angelova

    IPC: G06T7/55 ,  G06T7/246 ,  G06N3/088 ,  G06T3/00 ,  G06N3/045

    CPC classification number: G06T7/55 ,  G06T7/248 ,  G06N3/088 ,  G06T3/0093 ,  G06N3/045 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.

    NEURAL NETWORK MODELS USING PEER-ATTENTION
    29.
    发明申请
    NEURAL NETWORK MODELS USING PEER-ATTENTION 有权

    公开(公告)号：US20230114556A1

    公开(公告)日：2023-04-13

    申请号：US17909581

    申请日：2021-07-14

    Applicant: Google LLC

    Inventor： Michael Sahngwon Ryoo ,  Anthony Jacob Piergiovanni ,  Anelia Angelova

    IPC: G06V10/82 ,  G06V10/26

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a network input using a neural network to generate a network output. In one aspect, a method comprises processing a network input sing a neural network to generate a network output, where the neural network has multiple blocks, wherein each block is configured to process a block input to generate a block output, the method comprising, for each target block of the neural network: generating attention-weighted representations of multiple first block outputs, comprising, for each first block output: processing multiple second block outputs to generate attention factors; and generating the attention-weighted representation of each first block output by applying the respective attention factors to the corresponding first block output; and generating the target block input from the attention-weighted representations; and processing the target block input using the target block to generate a target block output.

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