  Lightweight or mobile neural networks used for real-time computer vision
tasks contain fewer parameters than normal networks, which lead to a
constrained performance. In this work, we proposed a novel activation function
named Tanh Exponential Activation Function (TanhExp) which can improve the
performance for these networks on image classification task significantly. The
definition of TanhExp is f(x) = xtanh(e^x). We demonstrate the simplicity,
efficiency, and robustness of TanhExp on various datasets and network models
and TanhExp outperforms its counterparts in both convergence speed and
accuracy. Its behaviour also remains stable even with noise added and dataset
altered. We show that without increasing the size of the network, the capacity
of lightweight neural networks can be enhanced by TanhExp with only a few
training epochs and no extra parameters added.
