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Patent Agency Ranking
公开(公告)号：US11380034B2
公开(公告)日：2022-07-05
申请号：US16759689
申请日：2018-10-29
Applicant: GOOGLE LLC
Inventor： Stephan Gouws , Frederick Bertsch , Konstantinos Bousmalis , Amelie Royer , Kevin Patrick Murphy
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for semantically-consistent image style transfer. One of the methods includes: receiving an input source domain image; processing the source domain image using one or more source domain low-level encoder neural network layers to generate a low-level representation; processing the low-level representation using one more high-level encoder neural network layers to generate an embedding of the input source domain image; processing the embedding using one or more high-level decoder neural network layers to generate a high-level feature representation of features of the input source domain image; and processing the high-level feature representation of the features of the input source domain image using one or more target domain low-level decoder neural network layers to generate an output target domain image that is from the target domain but that has similar semantics to the input source domain image.
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公开(公告)号：US20210334624A1
公开(公告)日：2021-10-28
申请号：US17365939
申请日：2021-07-01
Applicant: Google LLC
Inventor： Wei Hua , Barret Zoph , Jonathon Shlens , Chenxi Liu , Jonathan Huang , Jia Li , Fei-Fei Li , Kevin Patrick Murphy
Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.
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公开(公告)号：US11087504B2
公开(公告)日：2021-08-10
申请号：US16494386
申请日：2018-05-21
Applicant: Google LLC
Inventor： Sergio Guadarrama Cotado , Jonathon Shlens , David Bieber , Mohammad Norouzi , Kevin Patrick Murphy , Ryan Lienhart Dahl
Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.
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公开(公告)号：US20200257961A1
公开(公告)日：2020-08-13
申请号：US16861491
申请日：2020-04-29
Applicant: Google LLC
Inventor： Wei Hua , Barret Zoph , Jonathon Shlens , Chenxi Liu , Jonathan Huang , Jia Li , Fei-Fei Li , Kevin Patrick Murphy
Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.
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公开(公告)号：US20200098144A1
公开(公告)日：2020-03-26
申请号：US16494386
申请日：2018-05-21
Applicant: Google LLC
Inventor： Mohammad Norouzi , Jonathon Shiens , David Bieber , Sergio Guadarrama Cotado , Kevin Patrick Murphy , Ryan Lienhart Dahl
Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.
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