A preservation-set-guided quantization and pruning loop improved test accuracy by up to 2.5% over unquantized models while keeping about 60% of the original size on an MNIST CNN and a character-level name decoder.
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Optimizing Deep Neural Networks using Safety-Guided Self Compression
A preservation-set-guided quantization and pruning loop improved test accuracy by up to 2.5% over unquantized models while keeping about 60% of the original size on an MNIST CNN and a character-level name decoder.