公开(公告)号：US20210027140A1

公开(公告)日：2021-01-28

申请号：US16338963

申请日：2017-10-06

申请人： Google LLC

发明人： Francois Chollet ,  Andrew Gerald Howard

IPC分类号： G06N3/04 ,  G06N3/08

摘要： A neural network system is configured to receive an input image and to generate a classification output for the input image. The neural network system includes: a separable convolution subnetwork comprising a plurality of separable convolutional neural network layers arranged in a stack one after the other, in which each separable convolutional neural network layer is configured to: separately apply both a depthwise convolution and a pointwise convolution during processing of an input to the separable convolutional neural network layer to generate a layer output.

公开(公告)号：US20230091374A1

公开(公告)日：2023-03-23

申请号：US17802060

申请日：2020-02-24

申请人： Google LLC

发明人： Qifei Wang ,  Alexander Kuznetsov ,  Alec Michael Go ,  Grace Chu ,  Eunyoung Kim ,  Feng Yang ,  Andrew Gerald Howard ,  Jeffrey M. Gilbert

IPC分类号： G06V30/413 ,  G06V10/22

摘要： The present disclosure is directed to object and/or character recognition for use in applications such as computer vision. Advantages of the present disclosure include lightweight functionality that can be used on devices such as smart phones. Aspects of the present disclosure include a sequential architecture where a lightweight machine-learned model can receive an image, detect whether an object is present in one or more regions of the image, and generate an output based on the detection. This output can be applied as a filter to remove image data that can be neglected for more memory intensive machine-learned models applied downstream.

公开(公告)号：US20190147318A1

公开(公告)日：2019-05-16

申请号：US15898566

申请日：2018-02-17

申请人： Google LLC

发明人： Andrew Gerald Howard ,  Mark Sandler ,  Liang-Chieh Chen ,  Andrey Zhmoginov ,  Menglong Zhu

IPC分类号： G06N3/04 ,  G06N3/08

摘要： 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.

公开(公告)号：US20210350206A1

公开(公告)日：2021-11-11

申请号：US17382503

申请日：2021-07-22

申请人： Google LLC

发明人： Andrew Gerald Howard ,  Mark Sandler ,  Liang-Chieh Chen ,  Andrey Zhmoginov ,  Menglong Zhu

IPC分类号： G06N3/04 ,  G06N3/08

摘要： 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.

公开(公告)号：US11593614B2

公开(公告)日：2023-02-28

申请号：US16338963

申请日：2017-10-06

申请人： Google LLC

发明人： Francois Chollet ,  Andrew Gerald Howard

IPC分类号： G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06V40/16 ,  G06V10/44

摘要： A neural network system is configured to receive an input image and to generate a classification output for the input image. The neural network system includes: a separable convolution subnetwork comprising a plurality of separable convolutional neural network layers arranged in a stack one after the other, in which each separable convolutional neural network layer is configured to: separately apply both a depthwise convolution and a pointwise convolution during processing of an input to the separable convolutional neural network layer to generate a layer output.

公开(公告)号：US11157815B2

公开(公告)日：2021-10-26

申请号：US16524410

申请日：2019-07-29

申请人： Google LLC

发明人： Andrew Gerald Howard ,  Bo Chen ,  Dmitry Kalenichenko ,  Tobias Christoph Weyand ,  Menglong Zhu ,  Marco Andreetto ,  Weijun Wang

IPC分类号： G06N3/08 ,  G06N3/04

摘要： The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.