  Binary Neural Networks emerged as a cost-effective and energy-efficient
solution for computer vision tasks by binarizing either network weights or
activations. However, common binary activations, such as the Sign activation
function, abruptly binarize the values with a single threshold, losing
fine-grained details in the feature outputs. This work proposes an activation
that applies multiple thresholds following dithering principles, shifting the
Sign activation function for each pixel according to a spatially periodic
threshold kernel. Unlike literature methods, the shifting is defined jointly
for a set of adjacent pixels, taking advantage of spatial correlations.
Experiments over the classification task demonstrate the effectiveness of the
designed dithering Sign activation function as an alternative activation for
binary neural networks, without increasing the computational cost. Further,
DeSign balances the preservation of details with the efficiency of binary
operations.
