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Binary Neural Networks (BNNs) are difficult to train, and suffer from drop ofaccuracy. It appears in practice that BNNs fail to train in the absence ofBatch Normalization (BatchNorm) layer. We find the main role of BatchNorm is toavoid exploding gradients in the case of BNNs. This finding suggests that thecommon initialization methods developed for full-precision networks areirrelevant to BNNs. We build a theoretical study on the role of BatchNorm inbinary training, backed up by numerical experiments.

How Does Batch Normalization Help Binary Training?