Hypothesis-Driven Deep Learning for Out of Distribution Detection

arXiv:2403.14058v1 Announce Type: new
Abstract: Predictions of opaque black-box systems are frequently deployed in high-stakes applications such as healthcare. For such applications, it is crucial to assess how models handle samples beyond the domain of training data. While several metrics and tests exist to detect out-of-distribution (OoD) data from in-distribution (InD) data to a deep neural network (DNN), their performance varies significantly across datasets, models, and tasks, which limits their practical use. In this paper, we propose a hypothesis-driven approach …

abstract applications arxiv beyond box cs.lg data deep learning detection distribution domain healthcare hypothesis metrics predictions samples stat.ml systems tests training training data type