# Outlier Exposure with Confidence Control for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/1906.03509 · HTML: https://mlsanity.com/paper/1906.03509/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding](https://mlsanity.com/paper/2108.10673/) (2021)
- [Towards Robust Deep Learning using Entropic Losses](https://mlsanity.com/paper/2208.03566/) (2022)

## Abstract (arXiv)

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.