Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation

TL;DR

This paper introduces a task-agnostic out-of-distribution detection method using kernel density estimation on intermediate features of pre-trained neural networks, applicable across classification and segmentation tasks, outperforming existing methods.

Contribution

It proposes a novel KDE-based approach for OOD detection that is task-agnostic and effective beyond image classification, including segmentation tasks.

Findings

Achieves high OOD detection performance across tasks.

Outperforms state-of-the-art methods in most cases.

Effective on both benchmark and medical imaging datasets.

Abstract

In the recent years, researchers proposed a number of successful methods to perform out-of-distribution (OOD) detection in deep neural networks (DNNs). So far the scope of the highly accurate methods has been limited to image level classification tasks. However, attempts for generally applicable methods beyond classification did not attain similar performance. In this paper, we address this limitation by proposing a simple yet effective task-agnostic OOD detection method. We estimate the probability density functions (pdfs) of intermediate features of a pre-trained DNN by performing kernel density estimation (KDE) on the training dataset. As direct application of KDE to feature maps is hindered by their high dimensionality, we use a set of lower-dimensional marginalized KDE models instead of a single high-dimensional one. At test time, we evaluate the pdfs on a test sample and produce a confidence score that indicates the sample is OOD. The use of KDE eliminates the need for making simplifying assumptions about the underlying feature pdfs and makes the proposed method task-agnostic. We perform extensive experiments on classification tasks using benchmark datasets for OOD detection. Additionally, we perform experiments on medical image segmentation tasks using brain MRI datasets. The results demonstrate that the proposed method consistently achieves high OOD detection performance in both classification and segmentation tasks and improves state-of-the-art in almost all cases. Code is available at \url{https://github.com/eerdil/task_agnostic_ood}