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24 mar 2018 año - QAT: Quantization and training of neural networks for efficient integer-arithmetic-only inference, CVPR

Descripción:

QAT employs the affine mapping of integers to real values with two constant parameters: Scale and Zeropoint. It first subtracts the Zero-point parameter from data (weights/activation), then divides the data by scale parameter, and finally getting the quantized results with rounding operation and affine mapping.

Añadido al timeline:

29 mar 2020
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fecha:

24 mar 2018 año
Ahora mismo
~ 7 years and 7 months ago