Fast image style transfers
First Claim
Patent Images
1. A method comprising:
- generating, using one or more processors of a client device, image data using a fully connected neural network layer that outputs the image data into a convolutional layer;
generating a stylized image from the image data using a convolutional neural network (CNN), the CNN configured with the convolution layer having rank one kernel matrices generated for a sample set of points of an inferred matrix configured to apply a style transfer of a style of one or more training images, the sample set of points comprising at least one element from each row and each column of the inferred matrix, an output of convolution layer generated at least in part by generating dot products of rank one image matrices from the image data and the rank one kernel matrices, the rank one image matrices from the image data being generated for the sample set of points of the inferred matrix; and
storing the stylized image in memory of the client device.
2 Assignments
0 Petitions
Accused Products
Abstract
Manipulating images using computationally expensive machine learning schemes can be implemented using server-generated models of the machine learning schemes that are transmitted to a client device for application. The schemes can include convolutional neural networks having a kernel comprising a plurality of low-rank matrices.
169 Citations
20 Claims
-
1. A method comprising:
-
generating, using one or more processors of a client device, image data using a fully connected neural network layer that outputs the image data into a convolutional layer; generating a stylized image from the image data using a convolutional neural network (CNN), the CNN configured with the convolution layer having rank one kernel matrices generated for a sample set of points of an inferred matrix configured to apply a style transfer of a style of one or more training images, the sample set of points comprising at least one element from each row and each column of the inferred matrix, an output of convolution layer generated at least in part by generating dot products of rank one image matrices from the image data and the rank one kernel matrices, the rank one image matrices from the image data being generated for the sample set of points of the inferred matrix; and storing the stylized image in memory of the client device. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A system comprising:
-
one or more processors of a machine; and a memory comprising instructions that, when executed by the one or more processors, cause the machine to perform operations comprising; generating image data using a fully connected neural network layer that outputs the image data into a convolutional layer; generating a stylized image from the image data using a convolutional neural network (CNN), the CNN configured with the convolution layer having rank one kernel matrices generated for a sample set of points of an inferred matrix configured to apply a style transfer of a style of one or more training images, the sample set of points comprising at least one element from each row and each column of the inferred matrix, an output of convolution layer generated at least in part by generating dot products of rank one image matrices from the image data and the rank one kernel matrices, the rank one image matrices from the image data being generated for the sample set of points of the inferred matrix; and storing the stylized image in the memory. - View Dependent Claims (9, 10, 11, 12, 13, 14)
-
-
15. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform operations comprising:
-
generating image data using a fully connected neural network layer that outputs the image data into a convolutional layer; generating a stylized image from the image data using a convolutional neural network (CNN), the CNN configured with the convolution layer having rank one kernel matrices generated for a sample set of points of an inferred matrix configured to apply a style transfer of a style of one or more training images, the sample set of points comprising at least one element from each row and each column of the inferred matrix, an output of convolution layer generated at least in part by generating dot products of rank one image matrices from the image data and the rank one kernel matrices, the rank one image matrices from the image data being generated for the sample set of points of the inferred matrix; and storing the stylized image in memory. - View Dependent Claims (16, 17, 18, 19, 20)
-
Specification