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About Global Sparse Momentum SGD For Pruning Very Deep Neural Networks
This document proposes a novel momentum-SGD-based optimization method called Global Sparse Momentum SGD (GSM) for pruning very deep neural networks. GSM aims to reduce network complexity by on-the-fly pruning while maintaining accuracy. It categorizes parameters into two parts at each iteration that are updated using different rules to gradually zero out redundant parameters based on a global compression ratio hyperparameter. This allows GSM to automatically find appropriate per-layer sparsity ratios and perform end-to-end training without re-training, addressing issues with prior methods. Experiments show GSM achieves high compression on image datasets and finds better "winning tickets" than prior approaches.
Detailed Information
| Author: | ['chuliang guo'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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