  Quantized neural network (NN) with a reduced bit precision is an effective
solution to reduces the computational and memory resource requirements and
plays a vital role in machine learning. However, it is still challenging to
avoid the significant accuracy degradation due to its numerical approximation
and lower redundancy. In this paper, a novel robustness-aware 2-bit
quantization scheme is proposed for NN base on binary NN and generative
adversarial network(GAN), witch improves the performance by enriching the
information of binary NN, efficiently extract the structural information and
considering the robustness of the quantized NN. Specifically, using shift
addition operation to replace the multiply-accumulate in the quantization
process witch can effectively speed the NN. Meanwhile, a structural loss
between the original NN and quantized NN is proposed to such that the
structural information of data is preserved after quantization. The structural
information learned from NN not only plays an important role in improving the
performance but also allows for further fine tuning of the quantization network
by applying the Lipschitz constraint to the structural loss. In addition, we
also for the first time take the robustness of the quantized NN into
consideration and propose a non-sensitive perturbation loss function by
introducing an extraneous term of spectral norm. The experiments are conducted
on CIFAR-10 and ImageNet datasets with popular NN( such as MoblieNetV2,
SqueezeNet, ResNet20, etc). The experimental results show that the proposed
algorithm is more competitive under 2-bit-precision than the state-of-the-art
quantization methods. Meanwhile, the experimental results also demonstrate that
the proposed method is robust under the FGSM adversarial samples attack.
