Reducing computations in a neural network may include determining a group including a plurality of convolution kernels of a convolution stage of a neural network. The convolution kernels of the group are similar to one another. A base convolution kernel for the group may be determined. Scaling factors for a plurality of input feature maps processed by the group may be calculated. The convolution stage of the neural network may be modified to calculate a composite input feature map using the scaling factors and apply the base convolution kernel to the composite input feature map.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: determining, using a processor, a group comprising a plurality of convolution kernels of a convolution stage of a neural network, wherein the convolution kernels of the group are similar to one another; determining a base convolution kernel for the group; determining scaling factors for a plurality of input feature maps processed by the group; and modifying the convolution stage to calculate a composite input feature map using the scaling factors and apply the base convolution kernel to the composite input feature map. 2. The method of claim 1, wherein determining the group of convolution kernels comprises: determining a similarity metric between a first convolution kernel and a second convolution kernel; and including the first convolution kernel and the second convolution kernel in the group responsive to determining that the similarity metric meets a similarity criterion. 3. The method of claim 2, wherein determining a similarity metric comprises: calculating a ratio of the first convolution kernel to the second convolution kernel; and calculating a standard deviation for the ratio of the first convolution kernel and the second convolution kernel. 4. The method of claim 1, wherein determining the group of convolution kernels comprises: selecting a convolution kernel from among a plurality of convolution kernels that operate on the feature map as a primary convolution kernel; and calculating a ratio of the primary convolution kernel to other convolution kernels of the plurality of convolution kernels not yet assigned to a group. 5. The method of claim 1, wherein determining the base convolution kernel for the group comprises: selecting a convolution kernel of the group as the base convolution kernel according to a base selection metric. 6. The method of claim 1, wherein determining the base convolution kernel for the group comprises: determining the base convolution kernel as a function of the convolution kernels of the group. 7. The method of claim 1, further comprising: retraining the neural network comprising the modified convolution stage without permitting changes to membership in the group of convolution kernels. 8. The method of claim 1, further comprising: executing the neural network comprising the modified convolution stage, wherein executing the neural network comprises: scaling each of the plurality of input feature maps using the scaling factors, generating the composite input feature map as a sum of the scaled input feature maps, and applying the base convolution kernel to the composite input feature map. 9. A system, comprising: a processor programmed to initiate executable operations comprising: determining a group comprising a plurality of convolution kernels of a convolution stage of a neural network, wherein the convolution kernels of the group are similar to one another; determining a base convolution kernel for the group; determining scaling factors for a plurality of input feature maps processed by the group; and modifying the convolution stage to calculate a composite input feature map using the scaling factors and apply the base convolution kernel to the composite input feature map. 10. The system of claim 9, wherein determining the group of convolution kernels comprises: determining a similarity metric between a first convolution kernel and a second convolution kernel; and including the first convolution kernel and the second convolution kernel in the group responsive to determining that the similarity metric meets a similarity criterion. 11. The system of claim 10, wherein determining a similarity metric comprises: calculating a ratio of the first convolution kernel to the second convolution kernel; and calculating a standard deviation for the ratio of the first convolution kernel and the second convolution kernel. 12. The system of claim 9, wherein determining the group of convolution kernels comprises: selecting a convolution kernel from among a plurality of convolution kernels that operate on the feature map as a primary convolution kernel; and calculating a ratio of the primary convolution kernel to other convolution kernels of the plurality of convolution kernels not yet assigned to a group. 13. The system of claim 9, wherein determining the base convolution kernel for the group comprises: selecting a convolution kernel of the group as the base convolution kernel according to a base selection metric. 14. The system of claim 9, wherein determining the base convolution kernel for the group comprises: determining the base convolution kernel as a function of the convolution kernels of the group. 15. The system of claim 9, wherein the processor is further programmed to initiate executable operations comprising: retraining the neural network comprising the modified convolution stage without permitting changes to membership in the group of convolution kernels. 16. A non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, perform a method comprising: determining, using the processor, a group comprising a plurality of convolution kernels of a convolution stage of a trained neural network, wherein the convolution kernels of the group are similar to one another; determining a base convolution kernel for the group; determining scaling factors for a plurality of input feature maps processed by the group; and modifying the convolution stage to calculate a composite input feature map using the scaling factors and apply the base convolution kernel to the composite input feature map. 17. The non-transitory computer-readable storage medium of claim 16, wherein determining the group of convolution kernels comprises: determining a similarity metric between a first convolution kernel and a second convolution kernel; and including the first convolution kernel and the second convolution kernel in the group responsive to determining that the similarity metric meets a similarity criterion. 18. The non-transitory computer-readable storage medium of claim 17, wherein determining a similarity metric comprises: calculating a ratio of the first convolution kernel to the second convolution kernel; and calculating a standard deviation for the ratio of the first convolution kernel and the second convolution kernel. 19. The non-transitory computer-readable storage medium of claim 16, wherein determining the group of convolution kernels comprises: selecting a convolution kernel from among a plurality of convolution kernels that operate on the feature map as a primary convolution kernel; and calculating a ratio of the primary convolution kernel to other convolution kernels of the plurality of convolution kernels not yet assigned to a group. 20. The non-transitory computer-readable storage medium of claim 16, wherein the method further comprises: retraining the neural network comprising the modified convolution stage without permitting changes to membership in the group of convolution kernels.