  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. We propose a novel loss function that gives rise
to a novel method, Outlier Exposure with Confidence Control (OECC), which
achieves superior results in OOD detection with OE both on image and text
classification tasks without requiring access to OOD samples. Additionally, we
experimentally show that the combination of OECC with state-of-the-art
post-training OOD detection methods, like the Mahalanobis Detector (MD) and the
Gramian Matrices (GM) methods, further improves their performance in the OOD
detection task, demonstrating the potential of combining training and
post-training methods for OOD detection.
