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COMPRESSION OF FULLY CONNECTED / RECURRENT LAYERS OF DEEP NETWORK(S) THROUGH ENFORCING SPATIAL LOCALITY TO WEIGHT MATRICES AND EFFECTING FREQUENCY COMPRESSION
COMPRESSION OF FULLY CONNECTED / RECURRENT LAYERS OF DEEP NETWORK(S) THROUGH ENFORCING SPATIAL LOCALITY TO WEIGHT MATRICES AND EFFECTING FREQUENCY COMPRESSION
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机译:通过确保权重矩阵的空间局部性和影响频率压缩来对深层网络的完全连接/递归层进行压缩
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摘要
A system, having a memory that stores computer executable components, and a processor that executes the computer executable components, reduces data size in connection with training a neural network by exploiting spatial locality to weight matrices and effecting frequency transformation and compression. A receiving component receives neural network data in the form of a compressed frequency-domain weight matrix. A segmentation component segments the initial weight matrix into original sub-components, wherein respective original sub-components have spatial weights. A sampling component applies a generalized weight distribution to the respective original sub-components to generate respective normalized sub-components. A transform component applies a transform to the respective normalized sub-components. A cropping component crops high-frequency weights of the respective transformed normalized sub-components to yield a set of low-frequency normalized sub-components to generate a compressed representation of the original sub-components.
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