  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}
