Artificial neural network
AssigneeNokia Technologies Oy
InventorXiaoheng JIANG
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Abstract
According to an example aspect of the present invention, there is provided a method, comprising: resizing a convolutional layer input of an artificial neural network with at least two different scales to obtain multiple groups of intermediate features maps, convolving the intermediate feature maps with a filter, resizing the convolution results to the size of the layer input, and concatenating the resized convolution results to form an output of the convolutional layer.
Claims17
1-19. (canceled)
A method, comprising:
- resizing a convolutional layer input of a convolutional artificial neural network with at least two different scales to obtain multiple groups of intermediate features maps,
- convolving the intermediate feature maps with a filter,
- resizing the convolution results to the size of the layer input, and
- concatenating the resized convolution results to form an output of the convolutional layer.
An apparatus comprising:
- memory configured to store data defining, at least partly, a convolutional artificial neural network, and
- at least one processing core configured to extract a convolutional layer of the convolutional artificial neural network by applying a filter for convolving a layer input at at least two different scales and concatenating resized convolution results.
A non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least:
- resize a convolutional layer input of a convolutional artificial neural network with at least two different scales to obtain multiple groups of intermediate features maps,
- convolve the intermediate feature maps with a filter,
- resize the convolution results to the size of the layer input, and
- concatenate the resized convolution results to form an output of the convolutional layer.
Dependencies are inferred from the claim text.