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

Beschreibung:

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.

Zugefügt zum Band der Zeit:

29 Mär 2020
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Datum:

24 März 2018 Jahr
Jetzt
~ 7 years and 6 months ago