  An activation function has crucial role in a deep neural network.
  A simple rectified linear unit (ReLU) are widely used for the activation
function.
  In this paper, a weighted sigmoid gate unit (WiG) is proposed as the
activation function.
  The proposed WiG consists of a multiplication of inputs and the weighted
sigmoid gate.
  It is shown that the WiG includes the ReLU and same activation functions as a
special case.
  Many activation functions have been proposed to overcome the performance of
the ReLU.
  In the literature, the performance is mainly evaluated with an object
recognition task.
  The proposed WiG is evaluated with the object recognition task and the image
restoration task.
  Then, the expeirmental comparisons demonstrate the proposed WiG overcomes the
existing activation functions including the ReLU.
