Binary Neural Networks (BNNs) have emerged as a promising solution for
reducing the memory footprint and compute costs of deep neural networks. BNNs,
on the other hand, suffer from information loss because binary activations are
limited to only two values, resulting in reduced accuracy. To improve the
accuracy, previous studies have attempted to control the distribution of binary
activation by manually shifting the threshold of the activation function or
making the shift amount trainable. During the process, they usually depended on
statistical information computed from a batch. We argue that using statistical
data from a batch fails to capture the crucial information for each input
instance in BNN computations, and the differences between statistical
information computed from each instance need to be considered when determining
the binary activation threshold of each instance. Based on the concept, we
propose the Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN),
which decides the activation threshold value considering the difference between
statistical data computed from a batch and each instance. The proposed
INSTA-BNN outperforms the baseline by 2.5% and 2.3% on the ImageNet
classification task with comparable computing cost, achieving 68.0% and 71.7%
top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.Comment: 19 pages, 7 figures; excluded axessibility packag