  Despite the achievements of recent binarization methods on reducing the
performance degradation of Binary Neural Networks (BNNs), gradient mismatching
caused by the Straight-Through-Estimator (STE) still dominates quantized
networks. This paper proposes a meta-based quantizer named QuantNet, which
utilizes a differentiable sub-network to directly binarize the full-precision
weights without resorting to STE and any learnable gradient estimators. Our
method not only solves the problem of gradient mismatching, but also reduces
the impact of discretization errors, caused by the binarizing operation in the
deployment, on performance. Generally, the proposed algorithm is implemented
within a fully differentiable framework, and is easily extended to the general
network quantization with any bits. The quantitative experiments on CIFAR-100
and ImageNet demonstrate that QuantNet achieves the signifficant improvements
comparing with previous binarization methods, and even bridges gaps of
accuracies between binarized models and full-precision models.
