  The application of machine learning in sciences has seen exciting advances in
recent years. As a widely applicable technique, anomaly detection has been long
studied in the machine learning community. Especially, deep neural nets-based
out-of-distribution detection has made great progress for high-dimensional
data. Recently, these techniques have been showing their potential in
scientific disciplines. We take a critical look at their applicative prospects
including data universality, experimental protocols, model robustness, etc. We
discuss examples that display transferable practices and domain-specific
challenges simultaneously, providing a starting point for establishing a novel
interdisciplinary research paradigm in the near future.
