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公开(公告)号：US11734545B2
公开(公告)日：2023-08-22
申请号：US15898566
申请日：2018-02-17
Applicant: Google LLC
Inventor： Andrew Gerald Howard , Mark Sandler , Liang-Chieh Chen , Andrey Zhmoginov , Menglong Zhu
Abstract: The present disclosure provides directed to new, more efficient neural network architectures. As one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. As another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. For example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. A residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
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公开(公告)号：US20190370648A1
公开(公告)日：2019-12-05
申请号：US16425900
申请日：2019-05-29
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Emmet Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US20240119256A1
公开(公告)日：2024-04-11
申请号：US18486534
申请日：2023-10-13
Applicant: Google LLC
Inventor： Andrew Gerald Howard , Mark Sandler , Liang-Chieh Chen , Andrey Zhmoginov , Menglong Zhu
Abstract: The present disclosure provides directed to new, more efficient neural network architectures. As one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. As another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. For example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. A residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
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公开(公告)号：US20210350206A1
公开(公告)日：2021-11-11
申请号：US17382503
申请日：2021-07-22
Applicant: Google LLC
Inventor： Andrew Gerald Howard , Mark Sandler , Liang-Chieh Chen , Andrey Zhmoginov , Menglong Zhu
Abstract: The present disclosure provides directed to new, more efficient neural network architectures. As one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. As another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. For example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. A residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
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公开(公告)号：US11823024B2
公开(公告)日：2023-11-21
申请号：US17382503
申请日：2021-07-22
Applicant: Google LLC
Inventor： Andrew Gerald Howard , Mark Sandler , Liang-Chieh Chen , Andrey Zhmoginov , Menglong Zhu
Abstract: The present disclosure provides directed to new, more efficient neural network architectures. As one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. As another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. For example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. A residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
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Patent Agency Ranking
公开(公告)号：US11074504B2
公开(公告)日：2021-07-27
申请号：US16611604
申请日：2018-11-14
Applicant: Google LLC
Inventor： Liang-Chieh Chen , Alexander Hermans , Georgios Papandreou , Gerhard Florian Schroff , Peng Wang , Hartwig Adam
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for instance segmentation. In one aspect, a system generates: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels. The system generates a region descriptor for each of the one or more regions, and provides the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate a predicted segmentation of the predicted type of object depicted in the region.
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公开(公告)号：US20210081796A1
公开(公告)日：2021-03-18
申请号：US17107745
申请日：2020-11-30
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US10853726B2
公开(公告)日：2020-12-01
申请号：US16425900
申请日：2019-05-29
Applicant: Google LLC
Inventor： Barret Zoph , Jonathon Shlens , Yukun Zhu , Maxwell Donald Emmet Collins , Liang-Chieh Chen , Gerhard Florian Schroff , Hartwig Adam , Georgios Papandreou
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.
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公开(公告)号：US20200175375A1
公开(公告)日：2020-06-04
申请号：US16611604
申请日：2018-11-14
Applicant: Google LLC
Inventor： Liang-Chieh Chen , Alexander Hermans , Georgios Papandreou , Gerhard Florian Schroff , Peng Wang , Hartwig Adam
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for instance segmentation. In one aspect, a system generates: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels. The system generates a region descriptor for each of the one or more regions, and provides the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate a predicted segmentation of the predicted type of object depicted in the region.
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公开(公告)号：US20190147318A1
公开(公告)日：2019-05-16
申请号：US15898566
申请日：2018-02-17
Applicant: Google LLC
Inventor： Andrew Gerald Howard , Mark Sandler , Liang-Chieh Chen , Andrey Zhmoginov , Menglong Zhu
Abstract: The present disclosure provides directed to new, more efficient neural network architectures. As one example, in some implementations, the neural network architectures of the present disclosure can include a linear bottleneck layer positioned structurally prior to and/or after one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. As another example, in some implementations, the neural network architectures of the present disclosure can include one or more inverted residual blocks where the input and output of the inverted residual block are thin bottleneck layers, while an intermediate layer is an expanded representation. For example, the expanded representation can include one or more convolutional layers, such as, for example, one or more depthwise separable convolutional layers. A residual shortcut connection can exist between the thin bottleneck layers that play a role of an input and output of the inverted residual block.
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