Simultaneous Classification and Out-of-Distribution Detection Using Deep Neural Networks

Aristotelis-Angelos Papadopoulos, Nazim Shaikh, Jiamian Wang, Mohammad Reza Rajati

Keywords: Out-of-Distribution Detection, OOD detection, Outlier Exposure, Classification, Open-World Classification, Anomaly Detection, Novelty Detection, Calibration, Neural Networks

TL;DR: 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.

Abstract: 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.

Code: https://www.dropbox.com/sh/1czixfxns51t9gu/AAD45AvxBFWOHx8RUFxFADbJa?dl=0