In this work, we study network binarization (i.e., binary neural networks, BNNs), which is one of the most promising techniques in compression for convolutional networks (CNNs). Although prior work has introduced many methods that improve accuracy BNNs by minimizing quantization error, there remains a non-negligible performance gap between binarized model and full-precision model. Given feature...