Deep neural networks have achieved great success in classiﬁcation tasks during the last years. However, one major problem to the path towards artiﬁcial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classiﬁcation algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efﬁciently detect out-of-distribution (OOD) examples without compromising much of its classiﬁcation accuracy on the test examples from known classes. Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classiﬁcation tasks. Additionally, the way this method was constructed makes it suitable for training any classiﬁcation algorithm that is based on Maximum Likelihood methods.
We propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with Outlier Exposure both on image and text classiﬁcation tasks. This paper tackles the problems of out-of-distribution detection and model calibration by adapting the loss function of the Outlier Exposure technique, with results demonstrating increased performance over OE on vision and text benchmarks and improved model calibration. Proposal for a new loss function to train the network with Outlier Exposure which leads to better OOD detection compared to simple loss functions using KL divergence.