  Binary neural networks (BNNs) constrain weights and activations to +1 or -1
with limited storage and computational cost, which is hardware-friendly for
portable devices. Recently, BNNs have achieved remarkable progress and been
adopted into various fields. However, the performance of BNNs is sensitive to
activation distribution. The existing BNNs utilized the Sign function with
predefined or learned static thresholds to binarize activations. This process
limits representation capacity of BNNs since different samples may adapt to
unequal thresholds. To address this problem, we propose a dynamic BNN (DyBNN)
incorporating dynamic learnable channel-wise thresholds of Sign function and
shift parameters of PReLU. The method aggregates the global information into
the hyper function and effectively increases the feature expression ability.
The experimental results prove that our method is an effective and
straightforward way to reduce information loss and enhance performance of BNNs.
The DyBNN based on two backbones of ReActNet (MobileNetV1 and ResNet18) achieve
71.2% and 67.4% top1-accuracy on ImageNet dataset, outperforming baselines by a
large margin (i.e., 1.8% and 1.5% respectively).
