A lightened neural network, method, and apparatus, and recognition method and apparatus implementing the same. A neural network includes a plurality of layers each comprising neurons and plural synapses connecting neurons included in neighboring layers. Synaptic weights with values greater than zero and less than a preset value of a variable a, which is greater than zero, may be at least partially set to zero. Synaptic weights with values greater than a preset value of a variable b, which is greater than zero, may be at least partially set to the preset value of the variable b.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network comprising: a plurality of layers each comprising neurons; and plural synapses connecting neurons included in neighboring layers, wherein synaptic weights with values greater than zero and less than a preset value of a variable a, which is greater than zero, are at least partially set to zero. 2. The neural network of claim 1, wherein the synaptic weights that are at least partially set to zero correspond to synapses of the plural synapses that connect the neighboring layers among the plurality of layers. 3. The neural network of claim 1, wherein synaptic weights with values greater than zero and greater than a preset value of a variable b, which is greater than the preset value of the variable a, are at least partially set to the preset value of the variable b. 4. The neural network of claim 3, wherein the synaptic weights with values greater than zero and greater than the preset value of the variable b, which are set to the preset value of the variable b, correspond to synapses of the plural synapses that connect the neighboring layers among the plurality of layers. 5. The neural network of claim 3, wherein the preset value of the variable b is independently preset for two or more of the plurality of layers. 6. The neural network of claim 3, wherein the preset value of the variable b is independently preset for two or more output map channels in a predetermined layer. 7. The neural network of claim 3, wherein each of synaptic weights with values greater than or equal to the preset value of the variable a and less than or equal to the preset value of the variable b is represented by a number of bits corresponding to log2(b−a) in which the variables a and b are integers. 8. The neural network of claim 1, wherein the preset value of the variable a is independently preset for two or more of the plurality of layers. 9. The neural network of claim 1, wherein the preset value of the variable a is independently preset for two or more output map channels in a predetermined layer. 10. The neural network of claim 1, wherein each of synaptic weights with values greater than or equal to the preset value of the variable a is represented by a number of bits corresponding to log2(max-a) in which max denotes a maximum synaptic weight greater than the preset value of the variable a and the variable a and max are integers. 11. A neural network comprising: a plurality of layers each comprising neurons; and plural synapses connecting neurons included in neighboring layers, wherein synaptic weights with values greater than a preset value of a variable b, which is greater than zero, are at least partially set to the preset value of the variable b. 12. The neural network of claim 11, wherein the synaptic weights that are at least partially set to the preset value of the variable b correspond to synapses of the plural synapses that connect the neighboring layers among the plurality of layers. 13. The neural network of claim 11, wherein the preset value of the variable b is different between at least two of the plurality of layers. 14. The neural network of claim 19, wherein the preset value of the variable b is different between at least two output map channels in a predetermined layer. 15. The neural network of claim 11, wherein each of synaptic weights with values less than or equal to the preset value of the variable b is represented by a number of bits corresponding to log2(b) in which the variable b is an integer. 16. A processor implemented recognition method comprising: acquiring regularized parameters corresponding to a layer for a neural network; deregularizing the regularized parameters based on a regularization variable corresponding to the layer; applying the deregularized parameters to the layer; and recognizing input data using the neural network with the layer resulting from the applying. 17. The recognition method of claim 16, wherein the regularization variable corresponding to the layer is independently set for two or more of a plurality of layers included in the neural network or for two or more of a plurality of output map channels included in the layer. 18. The recognition method of claim 16, wherein the regularization variable comprises an offset to shift the regularized parameters based on a value of zero. 19. The recognition method of claim 16, wherein the applying of the deregularized parameters comprises, in response to the deregularized parameters corresponding to m-bit integers and the neural network receiving an input of n-bit real numbers and n being greater than m, dequantizing the deregularized parameters to n-bit real numbers; and applying the dequantized parameters to the layer. 20. The recognition method of claim 16, wherein the applying of the deregularized parameters comprises: acquiring a bit sequence indicating whether a parameter has a value of zero from the layer; decompressing the deregularized parameters based on the bit sequence, the deregularized parameters forming a non-zero sequence; and applying the decompressed parameters to the layer. 21. The recognition method of claim 20, wherein the decompressing of the deregularized parameters comprises: determining a decompressed parameter of a first index in the bit sequence by multiplying a bit value of the first index and a parameter of a second index in the non-zero sequence; increasing the second index by the bit value of the first index; and increasing the first index by “1.” 22. A non-transitory computer-readable storage medium storing instructions, which when executed by a processor, cause the processor to implement the method of claim 16. 23. A recognition apparatus comprising: a processor configured to acquire regularized parameters corresponding to a layer for a neural network, deregularize the regularized parameters based on a regularization variable corresponding to the layer, apply the deregularized parameters to the layer, and recognize input data using the neural network with the layer resulting from the applying. 24. The recognition apparatus of claim 23, wherein the regularization variable corresponding to the layer is independently set for two or more of a plurality of layers included in the neural network or for two or more of a plurality of output map channels included in the layer. 25. The recognition apparatus of claim 23, wherein the regularization variable comprises an offset to shift the regularized parameters based on a value of zero. 26. The recognition apparatus of claim 23, wherein, to implement the applying of the deregularized parameters, the processor is configured to, in response to the deregularized parameters corresponding to m-bit integers and the neural network receiving an input of n-bit real numbers and n being greater than m, dequantize the deregularized parameters to n-bit real numbers and apply the dequantized parameters to the layer. 27. The recognition apparatus of claim 23, wherein, to implement the applying of the deregularized parameters, the processor is configured to acquire a bit sequence indicating whether a parameter has a value of zero from the layer, decompress the deregularized parameters based on the bit sequence, and apply the decompressed parameters to the layer, the deregularized parameters forming a non-zero sequence. 28. The recognition apparatus of claim 27, wherein, to implement the decompressing of the deregularized parameters, the processor is further configured to determine a decompressed parameter of a first index in the bit sequence by multiplying a bit value of the first index and a parameter of a second index in the non-zero sequence, increase the second index by the bit value of the first index, and increase the first index by “1,” to decompress the deregularized parameters.