Inhibited Softmax for Uncertainty Estimation in Neural Networks

Marcin Możejko, Mateusz Susik, Rafał Karczewski

Abstract: 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.

Keywords: uncertainty estimation, out-of-distribution detection, inhibited softmax

TL;DR: Uncertainty estimation in a single forward pass without additional learnable parameters.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/inhibited-softmax-for-uncertainty-estimation/code)