Dynamic ReLU

Rectified linear units (ReLU) are commonly used in deep neural networks. So far ReLU and its generalizations (either non-parametric or parametric) are static, performing identically for all input samples. In this paper, we propose Dynamic ReLU (DY-ReLU), a dynamic rectifier whose parameters are input-dependent as a hyper function over all input elements. The key insight is that DY-ReLU encodes the global context into the hyper function and adapts the piecewise linear activation function accordingly. Compared to its static counterpart, DY-ReLU has negligible extra computational cost, but significantly more representation capability, especially for light-weight neural networks. By simply using DY-ReLU for MobileNetV2, the top-1 accuracy on ImageNet classification is boosted from 72.0% to 76.2% with only 5% additional FLOPs.