A subjective-logic-based model uncertainty estimation mechanism for out-of-domain detection
Jiarui Xie, Thierry Château, Violaine Antoine · 2021
Deep neural networks are important for a wide range of scientific and industrial processes. However, a classical discriminative model always makes a classification with respect to the probabilities allocated to the training labels, even when the sample is out of the domain. Thus, it is of interest to assign uncertainty to a model prediction to avoid such a situation. Fortunately, there are many existing methods for dealing with this kind of problem, one branch of which involves combining neural networks with subjective logic (SL). Based on previous works, we propose a new method called subjective-logic-based uncertainty estimation (SLUE) that can take the base rate distribution explicitly into account to refine the Dirichlet distribution parameters and guide the model training. Experiments were performed on several public datasets and additional adversarial datasets. Compared with existed methods, SLUE reached better uncertainty assessment performance (15% improvement in terms of % max entropy) as well as comparable prediction accuracy performance.