Updating only normalization affine parameters under the target fake-quantization graph recovers severely degraded low-bit quantized models while training less than 1.43% of the parameters.
Proceedings of the 37th International Conference on Machine Learning (ICML) , series=
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Low-Dimensional High-Leverage Subspace Optimization: Beyond Full-Parameter Coupled Training for Neural Network Quantization
Updating only normalization affine parameters under the target fake-quantization graph recovers severely degraded low-bit quantized models while training less than 1.43% of the parameters.