  Binary Neural Networks (BNNs) are receiving an upsurge of attention for
bringing power-hungry deep learning towards edge devices. The traditional
wisdom in this space is to employ sign() for binarizing featuremaps. We argue
and illustrate that sign() is a uniqueness bottleneck, limiting information
propagation throughout the network. To alleviate this, we propose to dispense
sign(), replacing it with a learnable activation binarizer (LAB), allowing the
network to learn a fine-grained binarization kernel per layer - as opposed to
global thresholding. LAB is a novel universal module that can seamlessly be
integrated into existing architectures. To confirm this, we plug it into four
seminal BNNs and show a considerable performance boost at the cost of tolerable
increase in delay and complexity. Finally, we build an end-to-end BNN (coined
as LAB-BNN) around LAB, and demonstrate that it achieves competitive
performance on par with the state-of-the-art on ImageNet.
