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.
Uncertainty estimation in a single forward pass without additional learnable parameters. A new method for computing output uncertainty estimates in DNNs for classification problems that matches state-of-the-art methods for uncertainty estimation and outperforms them in out-of-distribution detection tasks. The authors present inhibited softmax, a modification of the softmax through adding a constant activation which provides a measure for uncertainty. 