Training Quantized Neural Networks with a Full-precision Auxiliary Module

CVPR 2020 Bohan ZhuangLingqiao LiuMingkui TanChunhua ShenIan Reid

In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a fullprecision auxiliary module... (read more)

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