Condense-expansion-depth-wise convolutional neural network for face recognition
AssigneeIntel Corporation
InventorsYurong CHEN, Jianguo LI
Life of this patent
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Abstract
Techniques related to implementing convolutional neural networks for face or other object recognition are discussed. Such techniques may include applying, in turn, a depth-wise separable convolution, a condense point-wise convolution, and an expansion point-wise convolution to input feature maps to generate output feature maps such that the output from the expansion point-wise convolution has more channels than the output from the condense point-wise convolution.
Claims21
1-25. (canceled)
A system for implementing a convolutional neural network (CNN) comprising:
- a memory to store input image data;
- and
- a processor coupled to the memory, the processor to:
- apply a depth-wise separable convolution to a plurality of input feature maps to generate a plurality of separate 2D feature maps, wherein the plurality of input feature maps are associated with the input image data;
- apply a condense point-wise convolution to the plurality of separate 2D feature maps to generate a first plurality of combined feature maps, wherein the first plurality of combined feature maps has a first number of channels;
- apply an expansion point-wise convolution to the first plurality of combined feature maps to generate a second plurality of combined feature maps, wherein the second plurality of combined feature maps has a second number of channels greater than the first number of channels;
- and
- output object recognition data corresponding to the input image data based at least in part on the second plurality of combined feature maps.
A computer-implemented method for implementing a convolutional neural network (CNN) comprising:
- applying a depth-wise separable convolution to a plurality of input feature maps to generate a plurality of separate 2D feature maps, wherein the plurality of input feature maps are associated with input image data;
- applying a condense point-wise convolution to the plurality of separate 2D feature maps to generate a first plurality of combined feature maps, wherein the first plurality of combined feature maps has a first number of channels;
- applying an expansion point-wise convolution to the first plurality of combined feature maps to generate a second plurality of combined feature maps, wherein the second plurality of combined feature maps has a second number of channels greater than the first number of channels;
- and
- outputting object recognition data corresponding to the input image data based at least in part on the second plurality of combined feature maps.
At least one machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to implement a convolutional neural network (CNN) by:
- applying a depth-wise separable convolution to a plurality of input feature maps to generate a plurality of separate 2D feature maps, wherein the plurality of input feature maps are associated with input image data;
- applying a condense point-wise convolution to the plurality of separate 2D feature maps to generate a first plurality of combined feature maps, wherein the first plurality of combined feature maps has a first number of channels;
- applying an expansion point-wise convolution to the first plurality of combined feature maps to generate a second plurality of combined feature maps, wherein the second plurality of combined feature maps has a second number of channels greater than the first number of channels;
- and
- outputting object recognition data corresponding to the input image data based at least in part on the second plurality of combined feature maps.
Dependencies are inferred from the claim text.