A method for generating a sparsified convolutional neural network (CNN) is provided that includes training the CNN to generate coefficient values of filters of convolution layers, and performing sparsified fine tuning on the convolution layers to generate the sparsified CNN, wherein the sparsified fine tuning causes selected nonzero coefficient values of the filters to be set to zero.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating a sparsified convolutional neural network (CNN), the method comprising: training the CNN to generate coefficient values of filters of convolution layers; and performing sparsified fine tuning on the convolution layers to generate the sparsified CNN, wherein the sparsified fine tuning causes selected nonzero coefficient values of the filters to be set to zero. 2. The method of claim 1, wherein performing sparsified fine tuning further comprises: determining a sparsity target for a subset of filters of a convolution layer; determining a maximum sparsity threshold for the subset; determining a sparsity threshold for the subset based on the sparsity target and the maximum sparsity threshold; and using the sparsity threshold to identify nonzero coefficient values of the subset to be set to zero. 3. The method of claim 2, wherein the subset comprises all filters of the convolution layer. 4. The method of claim 2, wherein the subset comprises all filters corresponding to an output feature map of the convolution layer. 5. The method of claim 2, wherein determining a maximum sparsity threshold further comprises: determining a maximum of absolute values of coefficients in the subset; and setting the maximum sparsity threshold to a fraction of the maximum. 6. The method of claim 2, wherein determining a sparsity threshold further comprises: computing sparsity of the subset at a current sparsity threshold value; and changing the sparsity threshold based on comparison of the sparsity to the maximum sparsity threshold and the sparsity target. 7. The method of claim 2, wherein the sparsity target is for all filters of all convolution layers of the CNN. 8. The method of claim 2, wherein a sparsity target for a first subset of filters of a first convolution layer is different from a sparsity target for a second subset of filters of a second convolution layer. 9. A computer system comprising: a processor; and a memory storing software instructions that, when executed by the processor, cause generation of a sparsified (CNN) wherein the CNN is trained to generate coefficient values of filters of convolution layers; and sparsified fine tuning on the convolution layers is performed to generate the sparsified CNN, wherein the sparsified fine tuning causes selected nonzero coefficient values of the filters to be set zero. 10. The computer system of claim 9, wherein the sparsified fine tuning further comprises: determination of a sparsity target for a subset of filters of a convolution layer; determination of a maximum sparsity threshold for the subset; determination of a sparsity threshold for the subset based on the sparsity target and the maximum sparsity threshold; and use of the sparsity threshold to identify nonzero coefficient values of the subset to be set to zero. 11. The computer system of claim 10, wherein the subset comprises all filters of the convolution layer. 12. The computer system of claim 10, wherein the subset comprises all filters for a feature map of the convolution layer. 13. The computer system of claim 10, wherein determination of a maximum sparsity threshold further comprises: determination of a maximum of absolute values of coefficients in the subset, wherein the maximum sparsity threshold is set to a fraction of the maximum. 14. The computer system of claim 10, wherein determination of a sparsity threshold further comprises: computation of sparsity of the subset at a current sparsity threshold; and modification of the sparsity threshold based on comparison of the sparsity to the maximum sparsity threshold and the sparsity target. 15. The computer system of claim 10, wherein the sparsity target is for all filters of all convolution layers of the CNN. 16. The computer system of claim 10, wherein a sparsity target for a first subset of filters of a first convolution layer is different from a sparsity target for a second subset of filters for a second convolution layer. 17. A computer readable medium storing software instructions that, when executed by one or more processors, cause generation of a sparsified CNN, the software instructions comprising instructions to: train a CNN to generate coefficient values of filters of convolution layers; and perform sparsified fine tuning on the convolution layers to generate the sparsified CNN, wherein the sparsified fine tuning causes selected nonzero coefficient values of the filters to be set zero. 18. The computer readable medium claim 17, wherein the instructions to perform sparsified fine tuning further comprise instructions to: determine a sparsity target for a subset of filters of a convolution layer; determine a maximum sparsity threshold for the subset; determine a sparsity threshold for the subset based on the sparsity target and the maximum sparsity threshold; and use the sparsity threshold to identify nonzero coefficient values of the subset to be set to zero. 19. The computer readable medium claim 18, wherein the instructions to determine a maximum sparsity threshold further comprise instructions to: determine a maximum of absolute values of coefficients in the subset; and set the maximum sparsity threshold to a fraction of the maximum. 20. The computer readable medium claim 18, wherein the instructions to determine a sparsity threshold further comprise instructions to: compute sparsity of the subset at a current sparsity threshold value; and change the sparsity threshold based on comparison of the sparsity to the maximum sparsity threshold and the sparsity target.