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Binary neural network leverages the Sign function to binarize real values, and its non-derivative property inevitably brings huge gradient errors during backpropagation. Although many hand-designed soft functions have been proposed to approximate gradients, their mechanism is not clear and there are still huge performance gaps between binary models and their full-precision counterparts. To address this, we propose to tackle network binarization as a binary classification problem and use aarXiv:2205.07433v2 fatcat:lpkedp6thjd25mqp6aau4evkre