Superpixel methods for convolutional neural networks
AssigneeGoogle Inc.
InventorsReginald Clifford Young, Jonathan Ross
Life of this patent
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Abstract
Methods, systems, and apparatus for efficiently performing a computation of a convolutional neural network layer. One of the methods includes transforming a X by Y by Z input tensor into a X′ by Y′ by Z′ input tensor, wherein X′ is smaller than or equal to X, Y′ is smaller than or equal to Y, and Z′ is larger than or equal to Z; obtaining one or more modified weight matrices, wherein the modified weight matrices operate on the X′ by Y′ by Z′ input tensor to generate a U′ by V′ by W′ output tensor, and the U′ by V′ by W′ output tensor comprises a transformed U by V by W output tensor; and processing the X′ by Y′ by Z′ input tensor using the modified weight matrices to generate the U′ by V′ by W′ output tensor.
Claims23
A computer implemented method for training a convolutional neural network that includes a plurality of convolutional neural network layers on training data,
- wherein the convolutional neural network comprises (i) at least one superpixel convolutional neural network layer having respective superpixel convolutional neural network layer parameters that is configured to receive an X by Y by Z input tensor and process the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor, and (ii) one or more other convolutional neural network layers having respective layer parameters,
- wherein the method comprises:
- obtaining training data;
- and
- training the convolutional neural network on the training data to adjust the values of the parameters of the superpixel convolutional neural network and the values of the parameters of the other convolutional neural network layers to trained values, and
- wherein receiving, by the superpixel convolutional neural network layer, an X by Y by Z input tensor and processing the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor comprises:
- transforming the X by Y by Z input tensor into a X′ by Y′ by Z′ input tensor, wherein X′ is smaller than or equal to X, Y′ is smaller than or equal to Y, and Z′ is larger than or equal to Z;
- obtaining one or more modified convolutional neural network layer weight matrices, wherein (i) the modified weight matrices operate on the X′ by Y′ by Z′ input tensor to generate a U′ by V′ by W′ output tensor, and (ii) the U′ by V′ by W′ output tensor comprises a transformed U by V by W output tensor, wherein U′ is smaller than or equal to U, V′ is smaller than or equal to V, and W′ is larger than or equal to W;
- and
- processing the X′ by Y′ by Z′ input tensor using the modified weight matrices to generate the U′ by V′ by W′ output tensor, wherein the U′ by V′ by W′ output tensor comprises the U by V by W output tensor.
A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for training a convolutional neural network that includes a plurality of convolutional neural network layers on training data,
- wherein the convolutional neural network comprises (i) at least one superpixel convolutional neural network layer having respective superpixel convolutional neural network layer parameters that is configured to receive an X by Y by Z input tensor and process the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor, and (ii) one or more other convolutional neural network layers having respective layer parameters, the operations comprising:
- obtaining training data;
- and
- training the convolutional neural network on the training data to adjust the values of the parameters of the superpixel convolutional neural network and the values of the parameters of the other convolutional neural network layers to trained values, and
- wherein receiving, by the superpixel convolutional neural network layer, an X by Y by Z input tensor and processing the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor comprises:
- transforming the X by Y by Z input tensor into a X′ by Y′ by Z′ input tensor, wherein X′ is smaller than or equal to X, Y′ is smaller than or equal to Y, and Z′ is larger than or equal to Z;
- obtaining one or more modified convolutional neural network layer weight matrices, wherein (i) the modified weight matrices operate on the X′ by Y′ by Z′ input tensor to generate a U′ by V′ by W′ output tensor, and (ii) the U′ by V′ by W′ output tensor comprises a transformed U by V by W output tensor, wherein U′ is smaller than or equal to U, V′ is smaller than or equal to V, and W′ is larger than or equal to W;
- and
- processing the X′ by Y′ by Z′ input tensor using the modified weight matrices to generate the U′ by V′ by W′ output tensor, wherein the U′ by V′ by W′ output tensor comprises the U by V by W output tensor.
A computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for training a convolutional neural network that includes a plurality of convolutional neural network layers on training data,
- wherein the convolutional neural network comprises (i) at least one superpixel convolutional neural network layer having respective superpixel convolutional neural network layer parameters that is configured to receive an X by Y by Z input tensor and process the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor, and (ii) one or more other convolutional neural network layers having respective layer parameters, the operations comprising:
- obtaining training data;
- and
- training the convolutional neural network on the training data to adjust the values of the parameters of the superpixel convolutional neural network and the values of the parameters of the other convolutional neural network layers to trained values, and
- wherein receiving, by the superpixel convolutional neural network layer, an X by Y by Z input tensor and processing the received input tensor using one or more convolutional neural network layer weight matrices to generate an U by V by W output tensor comprises:
- transforming the X by Y by Z input tensor into a X′ by Y′ by Z′ input tensor, wherein X′ is smaller than or equal to X, Y′ is smaller than or equal to Y, and Z′ is larger than or equal to Z;
- obtaining one or more modified convolutional neural network layer weight matrices, wherein (i) the modified weight matrices operate on the X′ by Y′ by Z′ input tensor to generate a U′ by V′ by W′ output tensor, and (ii) the U′ by V′ by W′ output tensor comprises a transformed U by V by W output tensor, wherein U′ is smaller than or equal to U, V′ is smaller than or equal to V, and W′ is larger than or equal to W;
- and
- processing the X′ by Y′ by Z′ input tensor using the modified weight matrices to generate the U′ by V′ by W′ output tensor, wherein the U′ by V′ by W′ output tensor comprises the U by V by W output tensor.
Dependencies are inferred from the claim text.