According to the present disclosure, a weight parameter of a neural network is divided into a plurality of portions having a certain size and approximation is individually performed on the portions using a weighted sum of the codebook vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing apparatus comprising: a division unit configured to divide a weight parameter of a neural network into a plurality of groups; and an encoding unit configured to approximate the weight parameter in accordance with a codebook and encode the weight parameter for individual divided groups. 2. The information processing apparatus according to claim 1, wherein the division unit divides the weight parameter into the plurality of groups after aligning the weight parameter by a predetermined method. 3. The information processing apparatus according to claim 1, wherein the weight parameter has elements of a binary value or a ternary value. 4. The information processing apparatus according to claim 1, wherein the neural network is a convolution neural network. 5. An information processing apparatus comprising: a storage unit configured to store a weight coefficient of a codebook and a codebook vector constituting the codebook; and a reconstruction unit configured to reconstruct a weight parameter of a neural network using the stored weight coefficient and the stored codebook vector. 6. The information processing apparatus according to claim 5, wherein the weight coefficient is obtained by optimizing a loss function including a loss term of approximation accuracy of the weight parameter of the neural network and a loss term as a sparse term of the weight coefficient in accordance with the codebook. 7. The information processing apparatus according to claim 5, wherein the storage unit stores a plurality of codebook sets, and the reconstruction unit reads and uses different codebook sets depending on a layer of the neural network which is a reconstruction target of the weight parameter. 8. The information processing apparatus according to claim 5, wherein at least one of the weight coefficient and the codebook vector has a binary value or a ternary value as an element. 9. The information processing apparatus according to claim 5, further comprising an instruction unit configured to allow a user to instruct a constraint condition on a learning parameter. 10. The information processing apparatus according to claim 5, wherein the neural network is a convolution neural network. 11. An information processing method comprising: dividing a weight parameter of a neural network into a plurality of groups; approximating the weight parameter in accordance with a codebook and encoding the weight parameter for individual divided groups. 12. An information processing method comprising: storing a weight coefficient of a codebook and a codebook vector constituting the codebook; and reconstructing a weight parameter of a neural network using the stored weight coefficient and the stored codebook vector. 13. A computer-readable storage medium storing a program which causes a computer to execute an information processing method, the method comprising: dividing a weight parameter of a neural network into a plurality of groups; and approximating the weight parameter in accordance with a codebook and encode the weight parameter for individual divided groups. 14. A computer-readable storage medium storing a program which causes a computer to execute an information processing method, the method comprising: storing a weight coefficient of a codebook and a codebook vector constituting the codebook in a storage unit; and reconstructing a weight parameter of a neural network using the stored weight coefficient and the stored codebook vector.