A method of training a neural network model includes determining a specificity of multiple filters after a predetermined number of training iterations. The method also includes training each of the filters based on the specificity.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a neural network model, comprising: determining a specificity of a plurality of filters after a predetermined number of training iterations; and training each filter of the plurality of filters based at least in part on the specificity. 2. The method of claim 1, further comprising determining whether to continue the training of each filter based at least in part on the specificity. 3. The method of claim 2, further comprising stopping training for a specific filter of the plurality of filters when the specificity of the specific filter is greater than a threshold. 4. The method of claim 2, further comprising stopping training of a specific filter when a change in the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 5. The method of claim 2, further comprising eliminating a specific filter from the neural network model when the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 6. The method of claim 5, further comprising continuing training of the neural network model after eliminating the specific filter. 7. The method of claim 1, in which the specificity is based at least in part on entropy, change from original values, variance weight values, difference from other filters, cross correlation with other filters, or a combination thereof. 8. The method of claim 1, in which the neural network model is trained while an error function is augmented with a pooled measure of the specificity of the plurality of filters. 9. The method of claim 1, further comprising determining a target complexity of the neural network model, based at least in part on memory specification, power specifications, or a combination thereof. 10. The method of claim 9, in which filters are selectively trained based at least in part on the determined target complexity, prioritizing filters to train based at least in part on the determined target complexity, or a combination thereof. 11. The method of claim 1, further comprising: prioritizing filters to apply to an input based at least in part on the specificity of each of the plurality of filters; and selecting a number of prioritized filters based at least in part on a target complexity of the neural network model. 12. The method of claim 11, in which the target complexity is based at least in part on memory specification, power specifications, or a combination thereof. 13. An apparatus for training a neural network model, comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor configured: to determine a specificity of a plurality of filters after a predetermined number of training iterations; and to train each filter of the plurality of filters based at least in part on the specificity. 14. The apparatus of claim 13, in which the at least one processor is further configured to determine whether to continue the training of each filter based at least in part on the specificity. 15. The apparatus of claim 14, in which the at least one processor is further configured to stop training for a specific filter of the plurality of filters when the specificity of the specific filter is greater than a threshold. 16. The apparatus of claim 14, in which the at least one processor is further configured to stop training of a specific filter when a change in the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 17. The apparatus of claim 14, in which the at least one processor is further configured to eliminate a specific filter from the neural network model when the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 18. The apparatus of claim 17, in which the at least one processor is further configured to continue training of the neural network model after eliminating the specific filter. 19. The apparatus of claim 13, in which the specificity is based at least in part on entropy, change from original values, variance weight values, difference from other filters, cross correlation with other filters, or a combination thereof. 20. The apparatus of claim 13, in which the at least one processor is further configured to train the neural network model while augmenting an error function with a pooled measure of the specificity of the plurality of filters. 21. The apparatus of claim 13, in which the at least one processor is further configured to determine a target complexity of the neural network model, based at least in part on memory specification, power specifications, or a combination thereof. 22. The apparatus of claim 21, in which the at least one processor is further configured to selectively train filters based at least in part on the determined target complexity, prioritizing filters to train based at least in part on the determined target complexity, or a combination thereof. 23. The apparatus of claim 13, in which the at least one processor is further configured: to prioritize filters to apply to an input based at least in part on the specificity of each of the plurality of filters; and to select a number of prioritized filters based at least in part on a target complexity of the neural network model. 24. The apparatus of claim 23, in which the target complexity is based at least in part on memory specification, power specifications, or a combination thereof. 25. A apparatus of training a neural network model, comprising: means for determining a specificity of a plurality of filters after a predetermined number of training iterations; and means for training each filter of the plurality of filters based at least in part on the specificity. 26. The apparatus of claim 25, further comprising means for determining whether to continue the training of each filter based at least in part on the specificity. 27. The apparatus of claim 26, further comprising means for stopping training for a specific filter of the plurality of filters when the specificity of the specific filter is greater than a threshold. 28. The apparatus of claim 26, further comprising means for stopping training of a specific filter when a change in the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 29. The apparatus of claim 26, further comprising means for eliminating a specific filter from the neural network model when the specificity of the specific filter is less than a threshold after the predetermined number of training iterations. 30. The apparatus of claim 29, further comprising means for continuing training of the neural network model after eliminating the specific filter. 31. The apparatus of claim 25, in which the specificity is based at least in part on entropy, change from original values, variance weight values, difference from other filters, cross correlation with other filters, or a combination thereof. 32. A non-transitory computer-readable medium for training a neural network model, the computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to determine a specificity of a plurality of filters after a predetermined number of training iterations; and program code train each filter of the plurality of filters based at least in part on the specificity.