The curious case of the odd OOD data

Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting “Out-of- Distribution” (OOD) data for deep learning systems continues to be an active research topic. We investigate nonparametric online and batch approaches for estimating distributional separation or “outlierness”. Using open source simulated and measured Synthetic Aperture RADAR (SAR) datasets, we empirically demonstrate that the concepts of OOD and “Out-of-Task” are not synonymous.