  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. Specifically, during training, we construct a mix-precision
network by augmenting the original low-precision network with the full
precision auxiliary module. Then the augmented mix-precision network and the
low-precision network are jointly optimized. This strategy creates additional
full-precision routes to update the parameters of the low-precision model, thus
making the gradient back-propagates more easily. At the inference time, we
discard the auxiliary module without introducing any computational complexity
to the low-precision network. We evaluate the proposed method on image
classification and object detection over various quantization approaches and
show consistent performance increase. In particular, we achieve near lossless
performance to the full-precision model by using a 4-bit detector, which is of
great practical value.
