Out of Distribution Detection with Null Space Analysis

Neural network development and training often relies on highly curated datasets. The use of curated data does not adequately take into account the possibility of encountering unexpected inputs or outliers. However, encountering outliers is common when deploying systems in real world applications. Since outliers are samples not drawn from the data distribution of interest, the diversity of outliers precludes the ability to train with fully representative outlier data. One mechanism to create more robust neural network systems that can gracefully handle outliers or unexpected inputs is to leverage out-of-distribution (OOD) detection. In this paper we introduce Null Space Analysis (NuSA) as a way to manipulate the null space inherent in many neural networks to detect OOD data. NuSA functions by minimizing the null space projections for inlier data, so that when OOD data is encountered a much larger null space projection will occur. Experiments show an improvement over existing state-of-the-art algorithms using a fixed architecture and several OOD datasets.