  We present a new method for uncertainty estimation and out-of-distribution
detection in neural networks with softmax output. We extend softmax layer with
an additional constant input. The corresponding additional output is able to
represent the uncertainty of the network. The proposed method requires neither
additional parameters nor multiple forward passes nor input preprocessing nor
out-of-distribution datasets. We show that our method performs comparably to
more computationally expensive methods and outperforms baselines on our
experiments from image recognition and sentiment analysis domains.
