ARSVD compresses pretrained CNNs to 10 to 20 percent fewer parameters while reporting F1 scores 0.1 to 0.5 points higher than the uncompressed baseline.
A computational-graph partitioning method for training memory- constrained dnns,
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Low-Rank Matrix Approximation for Neural Network Compression
ARSVD compresses pretrained CNNs to 10 to 20 percent fewer parameters while reporting F1 scores 0.1 to 0.5 points higher than the uncompressed baseline.