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》 资源 》论文》PROBABILISTIC CONNECTION IMPORTANCE INFER -ENCE AND LOSSLESS COMPRESSION OF DEEP NEU -RAL NETWORKS

PROBABILISTIC CONNECTION IMPORTANCE INFER -ENCE AND LOSSLESS COMPRESSION OF DEEP NEU -RAL NETWORKS

2019-12-30 | |  65 |   40 |   0

Abstract

Deep neural networks (DNNs) can be huge in size, requiring a considerable amount of energy and computational resources to operate, which limits their applications in numerous scenarios. It is thus of interest to compress DNNs while maintaining their performance levels. We here propose a probabilistic importance inference approach for pruning DNNs. Specifically, we test the significance of the relevance of a connection in a DNN to the DNN’s outputs using a nonparemetric scoring test and keep only those significant ones. Experimental results show that the proposed approach achieves better lossless compression rates than existing techniques.

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